- CARTA: How Language Evolves (2015, Symposia) [media link] [notes]
- Recent Trends in Deep Learning Based Natural Language Processing (Oct 2018) [arXiv] [notes]
- What Level of Quality can Neural Machine Translation Attain on Literary Text? (Jan 2018) [arXiv] [notes]
- Nematus: a Toolkit for Neural Machine Translation (Mar 2017) [arXiv] [notes]
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (Jul 2019) [arXiv] [Benchmark] [PyTorch] [notes]
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (Sep 2018) [arXiv] [Benchmark] [notes]
- Evaluating Natural Language Understanding Services for Conversational Question Answering Systems (Aug 2017, SIGDIAL 2017) [aclweb] [dataset] [notes]
- My Notes on Autoencoders and Variants [notes]
- Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder (Nov 2018, EMNLP 2018) [arXiv] [notes]
- ELMo: Deep contextualized word representations (Mar 2018, NAACL 2018, Allen Institute) [arXiv] [notes]
- How to Construct Deep Recurrent Neural Networks (Dec 2013) [arXiv] [notes]
- Read + Verify: Machine Reading Comprehension with Unanswerable Questions (Aug 2018, AAAI 2019, Microsoft) [arXiv] [notes]
- Neural Speed Reading via Skim-RNN (Mar 2018) [arXiv] [code] [notes]
- Look, Listen and Learn (May 2017) [arXiv] [TwoMinutePapers] [notes]
- Get To The Point: Summarization with Pointer-Generator Networks (Apr 2017) [arXiv] [code] [notes]
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My Notes on Attention and Self-Attention in NLP [notes]
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My Notes on Visual-Linguistic BERT-based models [notes]
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"NLP's ImageNet moment has arrived" (Jul 2018) [Blog] [notes]
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Attention Is All You Need [Transformer] (Dec 2017, NIPS 2017, Google Brain) [arXiv] [Harvard Blog] [tensor2tensor] [ppt] [notes]
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Oct 2018, Google AI Language) [arXiv] [Tensorflow, PyTorch] [notes]
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Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures (Nov 2018, EMNLP 2018) [arXiv] [notes]
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MT-DNN: Multi-Task Deep Neural Networks for Natural Language Understanding (May 2019, Microsoft) [arXiv] [notes]
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Reformer: The Efficient Transformer (ICLR 2020, Google AI) [OpenReview] [Tensorflow] [notes]
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DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference (ACL 2020, Vector Institute) [paper] [slideslive] [PyTorch] [notes]
- Pay Less Attention with Lightweight and Dynamic Convolutions (Sep 2018, ICLR 2019, Facebook AI Research) [OpenReview] [PyTorch] [notes]
- A GRU-Gated Attention Model for Neural Machine Translation (Apr 2017) [arXiv] [code] [notes]
- DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (Nov 2018, AAAI) [arXiv] [code] [ppt] [notes]
- Neural Quality Estimation of Grammatical Error Correction (Nov 2018, EMNLP 2018) [aclweb] [PyTorch] [notes]
- Simultaneous Identification of Tweet Purpose and Position (AAAI 2020) [arXiv] [notes]
- Fast Intent Classification for Spoken Language Understanding [BranchyNet Application] (arXiv, Dec 2019) [arXiv] [ppt] [notes]
- Subword Semantic Hashing for Intent Classification on Small Datasets (Dec 2018) [arXiv] [PyTorch] [ppt] [notes]
- My Notes on Solutions for Imbalanced data [notes]
- EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (Jan 2019) [arXiv] [code] [notes]
- SMOTE: Synthetic Minority Over-sampling Technique (Jun 2002, JAIR) [arXiv] [notes]
- ARCID: A new approach to deal with imbalanced datasets classification (Jan 2018, SOFSEM 2018) [paper] [notes]
- Don't Underestimate the Benefits of Being Misunderstood (2017) [WebMIT] [notes]
- Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation (Oct 2018) [arXiv] [notes]
- The challenge of realistic music generation: modelling raw audio at scale (Jun 2018) [arXiv] [notes]
- SampleRNN: An Unconditional End-to-End Neural Audio Generation Model (Feb 2017, ICLR) [arXiv] [code, pytorch] [notes]
- WaveNet: A Generative Model for Raw Audio (Sep 2016) [arXiv] [code] [notes]
- FFTNet: a Real-Time Speaker-Dependent Neural Vocoder (April 2018, ICASSP) [paper] [code] [notes]
- WaveRNN: Efficient Neural Audio Synthesis (June 2018, ICML) [arXiv] [code] [notes]
- Song From PI: A Musically Plausible Network for Pop Music Generation (Nov 2016) [arXiv] [website] [notes]
- Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions (Dec 2017) [arXiv] [notes]
- Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model (March 2017) [arXiv] [code1, code2] [notes]
- MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation (Mar 2017) [arXiv] [code] [notes]
- Multimodal Machine Learning: A Survey and Taxonomy (Aug 2017, PAMI 2018) [arXiv] [notes]
- Using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge (Nov 2018) [arXiv] [notes]
- Char2Wav: End-to-End Speech Synthesis (ICLR Workshop 2017) [OpenReview] [code] [notes]
- Generative Adversarial Network (Jun 2014) [arXiv] [code] [notes]
- Coupled Generative Adversarial Networks (Jun 2016) [arXiv] [code] [notes]
- Adversarial Autoencoders [Blog]
- Conditional Image Generation with PixelCNN Decoders (Jun 2016) [arXiv] [code] [notes]
- C-RNN-GAN: Continuous recurrent neural networks with adversarial training (Nov 2016) [arXiv] [code] [notes]
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Nov 2015) [arXiv] [code1, code2] [notes]
- A Neural Algorithm of Artistic Style [arXiv] [code1, code2, code3, code4] [notes]
- Video Pixel Networks (Oct 2016) [arXiv] [notes]
- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient (Sep 2016) [arXiv] [code1, code2, code3] [notes]
- SinGAN: Learning a Generative Model from a Single Natural Image (ICCV 2019, Best Paper) [paper] [video1, video2] [paper webpage] [PyTorch] [notes]
- i-RevNet: Deep Invertible Networks (ICLR 2018) [OpenReview] [PyTorch] [notes]
- The Reversible Residual Network: Backpropagation Without Storing Activations (NeurIPS 2017) [paper] [Tensorflow, PyTorch] [notes]
- How AI can save our humanity by Kai-Fu Lee [TEDTalk] [notes]
- Dynamic Routing Between Capsules (Oct 2017) [arXiv] [Siraj Raval] [notes]
- GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism (2018, Google Brain) [arXiv] [notes]
- The Learning Machine [blog] [codes]
- Browse SoTA
- DeepMind
- Distill Journal: Online journal that doesn't limit your work to a pdf, it allows the reader to have a more interactive experience
- AI: Kaggle and Numerai
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Towards Data Science: Learn Docker