1 |
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning |
2 |
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data |
3 |
Deep Subspace Clustering Networks |
4 |
Attentional Pooling for Action Recognition |
5 |
On the Consistency of Quick Shift |
6 |
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization |
7 |
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis |
8 |
Dilated Recurrent Neural Networks |
9 |
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs |
10 |
Scalable Generalized Linear Bandits: Online Computation and Hashing |
11 |
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models |
12 |
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent |
13 |
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning |
14 |
Interactive Submodular Bandit |
15 |
Learning to See Physics via Visual De-animation |
16 |
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks |
17 |
Decoding with Value Networks for Neural Machine Translation |
18 |
Parametric Simplex Method for Sparse Learning |
19 |
Group Sparse Additive Machine |
20 |
Uprooting and Rerooting Higher-Order Graphical Models |
21 |
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings |
22 |
From Parity to Preference-based Notions of Fairness in Classification |
23 |
Inferring Generative Model Structure with Static Analysis |
24 |
Structured Embedding Models for Grouped Data |
25 |
A Linear-Time Kernel Goodness-of-Fit Test |
26 |
Cortical microcircuits as gated-recurrent neural networks |
27 |
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms |
28 |
A simple model of recognition and recall memory |
29 |
On Structured Prediction Theory with Calibrated Convex Surrogate Losses |
30 |
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model |
31 |
MaskRNN: Instance Level Video Object Segmentation |
32 |
Gated Recurrent Convolution Neural Network for OCR |
33 |
Towards Accurate Binary Convolutional Neural Network |
34 |
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks |
35 |
Learning a Multi-View Stereo Machine |
36 |
Phase Transitions in the Pooled Data Problem |
37 |
Universal Style Transfer via Feature Transforms |
38 |
On the Model Shrinkage Effect of Gamma Process Edge Partition Models |
39 |
Pose Guided Person Image Generation |
40 |
Inference in Graphical Models via Semidefinite Programming Hierarchies |
41 |
Variable Importance Using Decision Trees |
42 |
Preventing Gradient Explosions in Gated Recurrent Units |
43 |
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems |
44 |
f-GANs in an Information Geometric Nutshell |
45 |
Toward Multimodal Image-to-Image Translation |
46 |
Mixture-Rank Matrix Approximation for Collaborative Filtering |
47 |
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms |
48 |
Learning with Average Top-k Loss |
49 |
Learning multiple visual domains with residual adapters |
50 |
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions |
51 |
Learning Spherical Convolution for Fast Features from 360° Imagery |
52 |
MarrNet: 3D Shape Reconstruction via 2.5D Sketches |
53 |
Multimodal Learning and Reasoning for Visual Question Answering |
54 |
Adversarial Surrogate Losses for Ordinal Regression |
55 |
Hypothesis Transfer Learning via Transformation Functions |
56 |
Controllable Invariance through Adversarial Feature Learning |
57 |
Convergence Analysis of Two-layer Neural Networks with ReLU Activation |
58 |
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization |
59 |
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks |
60 |
Efficient Online Linear Optimization with Approximation Algorithms |
61 |
Geometric Descent Method for Convex Composite Minimization |
62 |
Diffusion Approximations for Online Principal Component Estimation and Global Convergence |
63 |
Avoiding Discrimination through Causal Reasoning |
64 |
Nonparametric Online Regression while Learning the Metric |
65 |
Recycling Privileged Learning and Distribution Matching for Fairness |
66 |
Safe and Nested Subgame Solving for Imperfect-Information Games |
67 |
Unsupervised Image-to-Image Translation Networks |
68 |
Coded Distributed Computing for Inverse Problems |
69 |
A Screening Rule for l1-Regularized Ising Model Estimation |
70 |
Improved Dynamic Regret for Non-degenerate Functions |
71 |
Learning Efficient Object Detection Models with Knowledge Distillation |
72 |
One-Sided Unsupervised Domain Mapping |
73 |
Deep Mean-Shift Priors for Image Restoration |
74 |
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees |
75 |
A New Theory for Matrix Completion |
76 |
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes |
77 |
Lower bounds on the robustness to adversarial perturbations |
78 |
Minimizing a Submodular Function from Samples |
79 |
Introspective Classification with Convolutional Nets |
80 |
Label Distribution Learning Forests |
81 |
Unsupervised learning of object frames by dense equivariant image labelling |
82 |
Compression-aware Training of Deep Networks |
83 |
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces |
84 |
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs |
85 |
Detrended Partial Cross Correlation for Brain Connectivity Analysis |
86 |
Contrastive Learning for Image Captioning |
87 |
Safe Model-based Reinforcement Learning with Stability Guarantees |
88 |
Online multiclass boosting |
89 |
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation |
90 |
Learning Overcomplete HMMs |
91 |
GP CaKe: Effective brain connectivity with causal kernels |
92 |
Decoupling "when to update" from "how to update" |
93 |
Self-Normalizing Neural Networks |
94 |
Learning to Pivot with Adversarial Networks |
95 |
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions |
96 |
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples |
97 |
Differentiable Learning of Submodular Functions |
98 |
Inductive Representation Learning on Large Graphs |
99 |
Subset Selection and Summarization in Sequential Data |
100 |
Question Asking as Program Generation |
101 |
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces |
102 |
Gradient Descent Can Take Exponential Time to Escape Saddle Points |
103 |
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction |
104 |
One-Shot Imitation Learning |
105 |
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding |
106 |
Integration Methods and Optimization Algorithms |
107 |
Sharpness, Restart and Acceleration |
108 |
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition |
109 |
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations |
110 |
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data |
111 |
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications |
112 |
Predictive-State Decoders: Encoding the Future into Recurrent Networks |
113 |
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds |
114 |
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results |
115 |
Matching neural paths: transfer from recognition to correspondence search |
116 |
Linearly constrained Gaussian processes |
117 |
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data |
118 |
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets |
119 |
Learning to Inpaint for Image Compression |
120 |
Adaptive Bayesian Sampling with Monte Carlo EM |
121 |
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization |
122 |
Shape and Material from Sound |
123 |
Flexible statistical inference for mechanistic models of neural dynamics |
124 |
Online Prediction with Selfish Experts |
125 |
Tensor Biclustering |
126 |
DPSCREEN: Dynamic Personalized Screening |
127 |
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach |
128 |
Testing and Learning on Distributions with Symmetric Noise Invariance |
129 |
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering |
130 |
Deanonymization in the Bitcoin P2P Network |
131 |
Accelerated consensus via Min-Sum Splitting |
132 |
Generalized Linear Model Regression under Distance-to-set Penalties |
133 |
Adaptive stimulus selection for optimizing neural population responses |
134 |
Nonbacktracking Bounds on the Influence in Independent Cascade Models |
135 |
Learning with Feature Evolvable Streams |
136 |
Online Convex Optimization with Stochastic Constraints |
137 |
Max-Margin Invariant Features from Transformed Unlabelled Data |
138 |
Regularized Modal Regression with Applications in Cognitive Impairment Prediction |
139 |
Translation Synchronization via Truncated Least Squares |
140 |
From which world is your graph |
141 |
A New Alternating Direction Method for Linear Programming |
142 |
Regret Analysis for Continuous Dueling Bandit |
143 |
Best Response Regression |
144 |
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning |
145 |
Learning Affinity via Spatial Propagation Networks |
146 |
Linear regression without correspondence |
147 |
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features |
148 |
Cost efficient gradient boosting |
149 |
Probabilistic Rule Realization and Selection |
150 |
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions |
151 |
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis |
152 |
Learning Multiple Tasks with Multilinear Relationship Networks |
153 |
Deep Hyperalignment |
154 |
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes |
155 |
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure |
156 |
Deep Learning with Topological Signatures |
157 |
Predicting User Activity Level In Point Processes With Mass Transport Equation |
158 |
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues |
159 |
Deep Dynamic Poisson Factorization Model |
160 |
Positive-Unlabeled Learning with Non-Negative Risk Estimator |
161 |
Optimal Sample Complexity of M-wise Data for Top-K Ranking |
162 |
Counterfactual Gaussian Processes for Reliable Decision-making and What-if Reasoning |
163 |
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding |
164 |
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks |
165 |
Train longer, generalize better: closing the generalization gap in large batch training of neural networks |
166 |
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks |
167 |
Model evidence from nonequilibrium simulations |
168 |
Minimal Exploration in Structured Stochastic Bandits |
169 |
Learned D-AMP: Principled Neural Network based Compressive Image Recovery |
170 |
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding |
171 |
Adaptive Clustering through Semidefinite Programming |
172 |
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning |
173 |
Repeated Inverse Reinforcement Learning |
174 |
The Numerics of GANs |
175 |
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search |
176 |
Learning Chordal Markov Networks via Branch and Bound |
177 |
Revenue Optimization with Approximate Bid Predictions |
178 |
Solving Most Systems of Random Quadratic Equations |
179 |
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data |
180 |
Lookahead Bayesian Optimization with Inequality Constraints |
181 |
Hierarchical Methods of Moments |
182 |
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts |
183 |
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network |
184 |
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization |
185 |
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models |
186 |
Generating steganographic images via adversarial training |
187 |
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration |
188 |
PixelGAN Autoencoders |
189 |
Consistent Multitask Learning with Nonlinear Output Relations |
190 |
Alternating minimization for dictionary learning with random initialization |
191 |
Learning ReLUs via Gradient Descent |
192 |
Stabilizing Training of Generative Adversarial Networks through Regularization |
193 |
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems |
194 |
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs |
195 |
Compatible Reward Inverse Reinforcement Learning |
196 |
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization |
197 |
Hiding Images in Plain Sight: Deep Steganography |
198 |
Neural Program Meta-Induction |
199 |
Bayesian Dyadic Trees and Histograms for Regression |
200 |
A graph-theoretic approach to multitasking |
201 |
Consistent Robust Regression |
202 |
Natural Value Approximators: Learning when to Trust Past Estimates |
203 |
Bandits Dueling on Partially Ordered Sets |
204 |
Elementary Symmetric Polynomials for Optimal Experimental Design |
205 |
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols |
206 |
Training Deep Networks without Learning Rates Through Coin Betting |
207 |
Pixels to Graphs by Associative Embedding |
208 |
Runtime Neural Pruning |
209 |
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks |
210 |
MMD GAN: Towards Deeper Understanding of Moment Matching Network |
211 |
The Reversible Residual Network: Backpropagation Without Storing Activations |
212 |
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe |
213 |
Zap Q-Learning |
214 |
Expectation Propagation for t-Exponential Family Using q-Algebra |
215 |
Few-Shot Learning Through an Information Retrieval Lens |
216 |
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation |
217 |
Associative Embedding: End-to-End Learning for Joint Detection and Grouping |
218 |
Practical Locally Private Heavy Hitters |
219 |
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences |
220 |
Inhomogeneous Hypergraph Clustering with Applications |
221 |
Differentiable Learning of Logical Rules for Knowledge Base Reasoning |
222 |
Masked Autoregressive Flow for Density Estimation |
223 |
Non-convex Finite-Sum Optimization Via SCSG Methods |
224 |
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting |
225 |
An inner-loop free solution to inverse problems using deep neural networks |
226 |
OnACID: Online Analysis of Calcium Imaging Data in Real Time |
227 |
Collaborative PAC Learning |
228 |
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization |
229 |
Scalable Demand-Aware Recommendation |
230 |
SGD Learns the Conjugate Kernel Class of the Network |
231 |
Noise-Tolerant Interactive Learning Using Pairwise Comparisons |
232 |
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems |
233 |
Generative Local Metric Learning for Kernel Regression |
234 |
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications |
235 |
Fitting Low-Rank Tensors in Constant Time |
236 |
Deep Supervised Discrete Hashing |
237 |
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation |
238 |
How regularization affects the critical points in linear networks |
239 |
Fisher GAN |
240 |
Information-theoretic analysis of generalization capability of learning algorithms |
241 |
Sparse Approximate Conic Hulls |
242 |
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems |
243 |
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System |
244 |
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM |
245 |
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning |
246 |
Multitask Spectral Learning of Weighted Automata |
247 |
Multi-way Interacting Regression via Factorization Machines |
248 |
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network |
249 |
Practical Data-Dependent Metric Compression with Provable Guarantees |
250 |
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models |
251 |
Nonlinear random matrix theory for deep learning |
252 |
Parallel Streaming Wasserstein Barycenters |
253 |
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games |
254 |
Dual Discriminator Generative Adversarial Nets |
255 |
Dynamic Revenue Sharing |
256 |
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search |
257 |
VAIN: Attentional Multi-agent Predictive Modeling |
258 |
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms |
259 |
Differentially Private Empirical Risk Minimization Revisited: Faster and More General |
260 |
Variational Inference via \chi Upper Bound Minimization |
261 |
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning |
262 |
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning |
263 |
An Empirical Study on The Properties of Random Bases for Kernel Methods |
264 |
Bridging the Gap Between Value and Policy Based Reinforcement Learning |
265 |
Premise Selection for Theorem Proving by Deep Graph Embedding |
266 |
A Bayesian Data Augmentation Approach for Learning Deep Models |
267 |
Principles of Riemannian Geometry in Neural Networks |
268 |
Cold-Start Reinforcement Learning with Softmax Policy Gradient |
269 |
Online Dynamic Programming |
270 |
Alternating Estimation for Structured High-Dimensional Multi-Response Models |
271 |
Convolutional Gaussian Processes |
272 |
Estimation of the covariance structure of heavy-tailed distributions |
273 |
Mean Field Residual Networks: On the Edge of Chaos |
274 |
Decomposable Submodular Function Minimization: Discrete and Continuous |
275 |
Gauging Variational Inference |
276 |
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs |
277 |
Robust Estimation of Neural Signals in Calcium Imaging |
278 |
State Aware Imitation Learning |
279 |
Beyond Parity: Fairness Objectives for Collaborative Filtering |
280 |
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent |
281 |
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach |
282 |
Model-Powered Conditional Independence Test |
283 |
Deep Voice 2: Multi-Speaker Neural Text-to-Speech |
284 |
Variance-based Regularization with Convex Objectives |
285 |
Deep Lattice Networks and Partial Monotonic Functions |
286 |
Continual Learning with Deep Generative Replay |
287 |
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms |
288 |
Learning Causal Structures Using Regression Invariance |
289 |
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback |
290 |
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem |
291 |
Reinforcement Learning under Model Mismatch |
292 |
Hierarchical Attentive Recurrent Tracking |
293 |
Tomography of the London Underground: a Scalable Model for Origin-Destination Data |
294 |
Rotting Bandits |
295 |
Unbiased estimates for linear regression via volume sampling |
296 |
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search |
297 |
Adaptive Accelerated Gradient Converging Method under H"{o}lderian Error Bound Condition |
298 |
Stein Variational Gradient Descent as Gradient Flow |
299 |
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery |
300 |
Shallow Updates for Deep Reinforcement Learning |
301 |
LightGBM: A Highly Efficient Gradient Boosting Decision Tree |
302 |
Adversarial Ranking for Language Generation |
303 |
Regret Minimization in MDPs with Options without Prior Knowledge |
304 |
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee |
305 |
Graph Matching via Multiplicative Update Algorithm |
306 |
Dynamic Importance Sampling for Anytime Bounds of the Partition Function |
307 |
Is the Bellman residual a bad proxy? |
308 |
Generalization Properties of Learning with Random Features |
309 |
Differentially private Bayesian learning on distributed data |
310 |
Learning to Compose Domain-Specific Transformations for Data Augmentation |
311 |
Wasserstein Learning of Deep Generative Point Process Models |
312 |
Ensemble Sampling |
313 |
Language Modeling with Recurrent Highway Hypernetworks |
314 |
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter |
315 |
Bayesian Compression for Deep Learning |
316 |
Streaming Sparse Gaussian Process Approximations |
317 |
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning |
318 |
Sparse Embedded k-Means Clustering |
319 |
Dynamic-Depth Context Tree Weighting |
320 |
A Regularized Framework for Sparse and Structured Neural Attention |
321 |
Multi-output Polynomial Networks and Factorization Machines |
322 |
Clustering Billions of Reads for DNA Data Storage |
323 |
Multi-Objective Non-parametric Sequential Prediction |
324 |
A Universal Analysis of Large-Scale Regularized Least Squares Solutions |
325 |
Deep Sets |
326 |
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events |
327 |
Process-constrained batch Bayesian optimisation |
328 |
Spherical convolutions and their application in molecular modelling |
329 |
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding |
330 |
On Optimal Generalizability in Parametric Learning |
331 |
Near Optimal Sketching of Low-Rank Tensor Regression |
332 |
Tractability in Structured Probability Spaces |
333 |
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit |
334 |
Gaussian process based nonlinear latent structure discovery in multivariate spike train data |
335 |
Neural system identification for large populations separating “what” and “where” |
336 |
Certified Defenses for Data Poisoning Attacks |
337 |
Eigen-Distortions of Hierarchical Representations |
338 |
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization |
339 |
Unsupervised Sequence Classification using Sequential Output Statistics |
340 |
Subset Selection under Noise |
341 |
Collecting Telemetry Data Privately |
342 |
Concrete Dropout |
343 |
Adaptive Batch Size for Safe Policy Gradients |
344 |
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning |
345 |
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference |
346 |
Bayesian GAN |
347 |
Off-policy evaluation for slate recommendation |
348 |
A multi-agent reinforcement learning model of common-pool resource appropriation |
349 |
On the Optimization Landscape of Tensor Decompositions |
350 |
High-Order Attention Models for Visual Question Answering |
351 |
Sparse convolutional coding for neuronal assembly detection |
352 |
Quantifying how much sensory information in a neural code is relevant for behavior |
353 |
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks |
354 |
Reducing Reparameterization Gradient Variance |
355 |
Visual Reference Resolution using Attention Memory for Visual Dialog |
356 |
Joint distribution optimal transportation for domain adaptation |
357 |
Multiresolution Kernel Approximation for Gaussian Process Regression |
358 |
Collapsed variational Bayes for Markov jump processes |
359 |
Universal consistency and minimax rates for online Mondrian Forests |
360 |
Welfare Guarantees from Data |
361 |
Diving into the shallows: a computational perspective on large-scale shallow learning |
362 |
End-to-end Differentiable Proving |
363 |
Influence Maximization with \varepsilon-Almost Submodular Threshold Functions |
364 |
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations |
365 |
Variational Laws of Visual Attention for Dynamic Scenes |
366 |
Recursive Sampling for the Nystrom Method |
367 |
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning |
368 |
Dynamic Routing Between Capsules |
369 |
Incorporating Side Information by Adaptive Convolution |
370 |
Conic Scan-and-Cover algorithms for nonparametric topic modeling |
371 |
FALKON: An Optimal Large Scale Kernel Method |
372 |
Structured Generative Adversarial Networks |
373 |
Conservative Contextual Linear Bandits |
374 |
Variational Memory Addressing in Generative Models |
375 |
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm |
376 |
Scalable Levy Process Priors for Spectral Kernel Learning |
377 |
Deep Hyperspherical Learning |
378 |
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction |
379 |
On-the-fly Operation Batching in Dynamic Computation Graphs |
380 |
Nonlinear Acceleration of Stochastic Algorithms |
381 |
Optimized Pre-Processing for Discrimination Prevention |
382 |
YASS: Yet Another Spike Sorter |
383 |
Independence clustering (without a matrix) |
384 |
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders |
385 |
Adaptive Active Hypothesis Testing under Limited Information |
386 |
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly |
387 |
Successor Features for Transfer in Reinforcement Learning |
388 |
Counterfactual Fairness |
389 |
Prototypical Networks for Few-shot Learning |
390 |
Triple Generative Adversarial Nets |
391 |
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation |
392 |
Mapping distinct timescales of functional interactions among brain networks |
393 |
Multi-Armed Bandits with Metric Movement Costs |
394 |
Learning A Structured Optimal Bipartite Graph for Co-Clustering |
395 |
Learning Low-Dimensional Metrics |
396 |
The Marginal Value of Adaptive Gradient Methods in Machine Learning |
397 |
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification |
398 |
Deconvolutional Paragraph Representation Learning |
399 |
Random Permutation Online Isotonic Regression |
400 |
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning |
401 |
Inverse Filtering for Hidden Markov Models |
402 |
Non-parametric Structured Output Networks |
403 |
Learning Active Learning from Data |
404 |
VAE Learning via Stein Variational Gradient Descent |
405 |
Reconstructing perceived faces from brain activations with deep adversarial neural decoding |
406 |
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems |
407 |
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks |
408 |
Sobolev Training for Neural Networks |
409 |
Multi-Information Source Optimization |
410 |
Deep Reinforcement Learning from Human Preferences |
411 |
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks |
412 |
Policy Gradient With Value Function Approximation For Collective Multiagent Planning |
413 |
Adversarial Symmetric Variational Autoencoder |
414 |
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays |
415 |
A Minimax Optimal Algorithm for Crowdsourcing |
416 |
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach |
417 |
A Decomposition of Forecast Error in Prediction Markets |
418 |
Safe Adaptive Importance Sampling |
419 |
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net |
420 |
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication |
421 |
Unsupervised Learning of Disentangled Representations from Video |
422 |
Federated Multi-Task Learning |
423 |
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? |
424 |
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities |
425 |
Improved Graph Laplacian via Geometric Self-Consistency |
426 |
Dual Path Networks |
427 |
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers |
428 |
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks |
429 |
Distral: Robust multitask reinforcement learning |
430 |
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions |
431 |
Trimmed Density Ratio Estimation |
432 |
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems |
433 |
Visual Interaction Networks: Learning a Physics Simulator from Video |
434 |
Reconstruct & Crush Network |
435 |
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach |
436 |
Simple strategies for recovering inner products from coarsely quantized random projections |
437 |
Discovering Potential Correlations via Hypercontractivity |
438 |
Doubly Stochastic Variational Inference for Deep Gaussian Processes |
439 |
Ranking Data with Continuous Labels through Oriented Recursive Partitions |
440 |
Scalable Model Selection for Belief Networks |
441 |
Targeting EEG/LFP Synchrony with Neural Nets |
442 |
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs |
443 |
Non-Stationary Spectral Kernels |
444 |
Overcoming Catastrophic Forgetting by Incremental Moment Matching |
445 |
Balancing information exposure in social networks |
446 |
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud |
447 |
Query Complexity of Clustering with Side Information |
448 |
QMDP-Net: Deep Learning for Planning under Partial Observability |
449 |
Robust Optimization for Non-Convex Objectives |
450 |
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation |
451 |
Adaptive Classification for Prediction Under a Budget |
452 |
Convergence rates of a partition based Bayesian multivariate density estimation method |
453 |
Affine-Invariant Online Optimization and the Low-rank Experts Problem |
454 |
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization |
455 |
A Unified Approach to Interpreting Model Predictions |
456 |
Stochastic Approximation for Canonical Correlation Analysis |
457 |
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice |
458 |
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions |
459 |
Scalable Variational Inference for Dynamical Systems |
460 |
Context Selection for Embedding Models |
461 |
Working hard to know your neighbor's margins: Local descriptor learning loss |
462 |
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex |
463 |
Multi-Task Learning for Contextual Bandits |
464 |
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon |
465 |
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds |
466 |
Selective Classification for Deep Neural Networks |
467 |
Minimax Estimation of Bandable Precision Matrices |
468 |
Monte-Carlo Tree Search by Best Arm Identification |
469 |
Group Additive Structure Identification for Kernel Nonparametric Regression |
470 |
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval |
471 |
Hash Embeddings for Efficient Word Representations |
472 |
Online Learning for Multivariate Hawkes Processes |
473 |
Maximum Margin Interval Trees |
474 |
DropoutNet: Addressing Cold Start in Recommender Systems |
475 |
A simple neural network module for relational reasoning |
476 |
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes |
477 |
Online Reinforcement Learning in Stochastic Games |
478 |
Position-based Multiple-play Bandit Problem with Unknown Position Bias |
479 |
Active Exploration for Learning Symbolic Representations |
480 |
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling |
481 |
Fair Clustering Through Fairlets |
482 |
Polynomial time algorithms for dual volume sampling |
483 |
Hindsight Experience Replay |
484 |
Stochastic and Adversarial Online Learning without Hyperparameters |
485 |
Teaching Machines to Describe Images with Natural Language Feedback |
486 |
Perturbative Black Box Variational Inference |
487 |
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models |
488 |
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space |
489 |
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization |
490 |
Learning Graph Representations with Embedding Propagation |
491 |
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes |
492 |
A-NICE-MC: Adversarial Training for MCMC |
493 |
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models |
494 |
Real-Time Bidding with Side Information |
495 |
Saliency-based Sequential Image Attention with Multiset Prediction |
496 |
Variational Inference for Gaussian Process Models with Linear Complexity |
497 |
K-Medoids For K-Means Seeding |
498 |
Identifying Outlier Arms in Multi-Armed Bandit |
499 |
Online Learning with Transductive Regret |
500 |
Riemannian approach to batch normalization |
501 |
Self-supervised Learning of Motion Capture |
502 |
Triangle Generative Adversarial Networks |
503 |
PRUNE: Preserving Proximity and Global Ranking for Network Embedding |
504 |
Bayesian Optimization with Gradients |
505 |
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation |
506 |
Renyi Differential Privacy Mechanisms for Posterior Sampling |
507 |
Online Learning with a Hint |
508 |
Identification of Gaussian Process State Space Models |
509 |
Robust Imitation of Diverse Behaviors |
510 |
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent |
511 |
Local Aggregative Games |
512 |
A Sample Complexity Measure with Applications to Learning Optimal Auctions |
513 |
Thinking Fast and Slow with Deep Learning and Tree Search |
514 |
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms |
515 |
Improving the Expected Improvement Algorithm |
516 |
Hybrid Reward Architecture for Reinforcement Learning |
517 |
Approximate Supermodularity Bounds for Experimental Design |
518 |
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification |
519 |
AdaGAN: Boosting Generative Models |
520 |
Straggler Mitigation in Distributed Optimization Through Data Encoding |
521 |
Multi-View Decision Processes: The Helper-AI Problem |
522 |
A Greedy Approach for Budgeted Maximum Inner Product Search |
523 |
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks |
524 |
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models |
525 |
Task-based End-to-end Model Learning in Stochastic Optimization |
526 |
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching |
527 |
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting |
528 |
On the Complexity of Learning Neural Networks |
529 |
Hierarchical Implicit Models and Likelihood-Free Variational Inference |
530 |
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference |
531 |
Approximation and Convergence Properties of Generative Adversarial Learning |
532 |
From Bayesian Sparsity to Gated Recurrent Nets |
533 |
Min-Max Propagation |
534 |
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? |
535 |
Gradient descent GAN optimization is locally stable |
536 |
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks |
537 |
Dualing GANs |
538 |
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model |
539 |
Do Deep Neural Networks Suffer from Crowding? |
540 |
Learning from Complementary Labels |
541 |
Online control of the false discovery rate with decaying memory |
542 |
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes |
543 |
Discriminative State Space Models |
544 |
On Fairness and Calibration |
545 |
Imagination-Augmented Agents for Deep Reinforcement Learning |
546 |
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations |
547 |
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning |
548 |
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra |
549 |
Asynchronous Parallel Coordinate Minimization for MAP Inference |
550 |
Multiscale Quantization for Fast Similarity Search |
551 |
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space |
552 |
Improved Training of Wasserstein GANs |
553 |
Learning Populations of Parameters |
554 |
Clustering with Noisy Queries |
555 |
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods |
556 |
Training Quantized Nets: A Deeper Understanding |
557 |
Permutation-based Causal Inference Algorithms with Interventions |
558 |
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis |
559 |
Gradient Methods for Submodular Maximization |
560 |
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization |
561 |
The Importance of Communities for Learning to Influence |
562 |
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos |
563 |
Learning Neural Representations of Human Cognition across Many fMRI Studies |
564 |
A KL-LUCB algorithm for Large-Scale Crowdsourcing |
565 |
Collaborative Deep Learning in Fixed Topology Networks |
566 |
Fast-Slow Recurrent Neural Networks |
567 |
Learning Disentangled Representations with Semi-Supervised Deep Generative Models |
568 |
Self-Supervised Intrinsic Image Decomposition |
569 |
Exploring Generalization in Deep Learning |
570 |
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control |
571 |
Fader Networks:Manipulating Images by Sliding Attributes |
572 |
Action Centered Contextual Bandits |
573 |
Estimating Mutual Information for Discrete-Continuous Mixtures |
574 |
Attention is All you Need |
575 |
Recurrent Ladder Networks |
576 |
Parameter-Free Online Learning via Model Selection |
577 |
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction |
578 |
Unbounded cache model for online language modeling with open vocabulary |
579 |
Predictive State Recurrent Neural Networks |
580 |
Early stopping for kernel boosting algorithms: A general analysis with localized complexities |
581 |
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability |
582 |
Convolutional Phase Retrieval |
583 |
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma |
584 |
Gaussian Quadrature for Kernel Features |
585 |
Value Prediction Network |
586 |
A Learning Error Analysis for Structured Prediction with Approximate Inference |
587 |
Efficient Second-Order Online Kernel Learning with Adaptive Embedding |
588 |
Implicit Regularization in Matrix Factorization |
589 |
Optimal Shrinkage of Singular Values Under Random Data Contamination |
590 |
Countering Feedback Delays in Multi-Agent Learning |
591 |
Asynchronous Coordinate Descent under More Realistic Assumptions |
592 |
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls |
593 |
Hierarchical Clustering Beyond the Worst-Case |
594 |
Invariance and Stability of Deep Convolutional Representations |
595 |
Statistical Cost Sharing |
596 |
The Expressive Power of Neural Networks: A View from the Width |
597 |
Spectrally-normalized margin bounds for neural networks |
598 |
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes |
599 |
Population Matching Discrepancy and Applications in Deep Learning |
600 |
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains |
601 |
Boltzmann Exploration Done Right |
602 |
Learned in Translation: Contextualized Word Vectors |
603 |
Neural Discrete Representation Learning |
604 |
Generalizing GANs: A Turing Perspective |
605 |
Scalable Log Determinants for Gaussian Process Kernel Learning |
606 |
Poincaré Embeddings for Learning Hierarchical Representations |
607 |
Learning Combinatorial Optimization Algorithms over Graphs |
608 |
Robust Conditional Probabilities |
609 |
Learning with Bandit Feedback in Potential Games |
610 |
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments |
611 |
Communication-Efficient Distributed Learning of Discrete Distributions |
612 |
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles |
613 |
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness |
614 |
Matrix Norm Estimation from a Few Entries |
615 |
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons |
616 |
Causal Effect Inference with Deep Latent-Variable Models |
617 |
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity |
618 |
Gradient Episodic Memory for Continual Learning |
619 |
Effective Parallelisation for Machine Learning |
620 |
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding |
621 |
Clustering Stable Instances of Euclidean k-means. |
622 |
Good Semi-supervised Learning That Requires a Bad GAN |
623 |
On Blackbox Backpropagation and Jacobian Sensing |
624 |
Protein Interface Prediction using Graph Convolutional Networks |
625 |
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities |
626 |
Towards Generalization and Simplicity in Continuous Control |
627 |
Random Projection Filter Bank for Time Series Data |
628 |
Filtering Variational Objectives |
629 |
On Frank-Wolfe and Equilibrium Computation |
630 |
Modulating early visual processing by language |
631 |
Learning Mixture of Gaussians with Streaming Data |
632 |
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction |
633 |
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium |
634 |
The Scaling Limit of High-Dimensional Online Independent Component Analysis |
635 |
Approximation Algorithms for \ell_0-Low Rank Approximation |
636 |
The power of absolute discounting: all-dimensional distribution estimation |
637 |
Few-Shot Adversarial Domain Adaptation |
638 |
Spectral Mixture Kernels for Multi-Output Gaussian Processes |
639 |
Neural Expectation Maximization |
640 |
Learning Linear Dynamical Systems via Spectral Filtering |
641 |
Z-Forcing: Training Stochastic Recurrent Networks |
642 |
Learning Hierarchical Information Flow with Recurrent Neural Modules |
643 |
Neural Variational Inference and Learning in Undirected Graphical Models |
644 |
Subspace Clustering via Tangent Cones |
645 |
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process |
646 |
Inverse Reward Design |
647 |
Structured Bayesian Pruning via Log-Normal Multiplicative Noise |
648 |
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin |
649 |
Acceleration and Averaging in Stochastic Descent Dynamics |
650 |
Kernel functions based on triplet comparisons |
651 |
An Error Detection and Correction Framework for Connectomics |
652 |
Style Transfer from Non-Parallel Text by Cross-Alignment |
653 |
Cross-Spectral Factor Analysis |
654 |
Stochastic Submodular Maximization: The Case of Coverage Functions |
655 |
Affinity Clustering: Hierarchical Clustering at Scale |
656 |
Unsupervised Transformation Learning via Convex Relaxations |
657 |
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening |
658 |
Linear Time Computation of Moments in Sum-Product Networks |
659 |
A Meta-Learning Perspective on Cold-Start Recommendations for Items |
660 |
Predicting Scene Parsing and Motion Dynamics in the Future |
661 |
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference |
662 |
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification |
663 |
Kernel Feature Selection via Conditional Covariance Minimization |
664 |
Convergence of Gradient EM on Multi-component Mixture of Gaussians |
665 |
Real Time Image Saliency for Black Box Classifiers |
666 |
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples |
667 |
Efficient and Flexible Inference for Stochastic Systems |
668 |
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent |
669 |
Active Learning from Peers |
670 |
Experimental Design for Learning Causal Graphs with Latent Variables |
671 |
Learning to Model the Tail |
672 |
Stochastic Mirror Descent in Variationally Coherent Optimization Problems |
673 |
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models |
674 |
Maxing and Ranking with Few Assumptions |
675 |
On clustering network-valued data |
676 |
A General Framework for Robust Interactive Learning |
677 |
Multi-view Matrix Factorization for Linear Dynamical System Estimation |