If you want to sync papers.
python scripts/sync_papers --sync_path {path_name}
- bold : important
tag: keyword- paper, article, note and code
- Gaussian Process
Supervised,Regression- note
- Importance Sampling
Approximate- notes
- Information Theory: A Tutorial Introduction (2018. 2)
Shannon's Theory- arXiv
Deep Learning (2015) Review
- nature, note
- Explaining and Harnessing Adversarial Examples (2014. 12)
FGSM (Fast Gradient Sign Method),Adversarial Training- arXiv
- The Limitations of Deep Learning in Adversarial Settings (2015. 11)
JSMA (Jacobian-based Saliency Map Approach),Adversarial Training- arXiv
- Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization (2015. 11)
Adversarial Training (generated adversarial examples),Proactive Defense- arXiv
- Practical Black-Box Attacks against Machine Learning (2016. 2)
Black-Box (No Access to Gradient),Generate Synthetic- arXiv
- Adversarial Patch (2017. 12)
Patch,White Box,Black Box- arXiv, the_morning_paper
- Machine Theory of Mind (2018. 2)
ToMnet,Meta-Learning,General Model,Agent- arXiv
- Building Machines That Learn and Think Like People (2016. 4)
Human-Like,Learn,Think- arXiv, note, the morning paper
- Network In Network (2013. 12)
- Fractional Max-Pooling (2014. 12)
- Deep Residual Learning for Image Recognition (2015. 12)
- Spherical CNNs (2018. 1)
Spherical Correlation,3D Model,Fast Fourier Transform (FFT)- arXiv, open_review
- Taskonomy: Disentangling Task Transfer Learning (2018. 4)
Taskonomy,Transfer Learning,Computational modeling of task relations- arXiv
- AutoAugment: Learning Augmentation Policies from Data (2018. 5)
Search Algorithm (RL),Sub-Policy- arXiv
- Exploring Randomly Wired Neural Networks for Image Recognition (2019. 4)
Randomly wired neural networks,Random Graph Models (ER, BA and WS)- arXiv
- MixMatch: A Holistic Approach to Semi-Supervised Learning (2019. 5)
MixMatch,Semi-Supervised,Augumentation -> Label Guessing -> Average -> Sharpening- arXiv
- Snorkel: Rapid Training Data Creation with Weak Supervision (2017. 11)
Labelling Functions,Data Programming- arXiv, the_morning_blog
- Training classifiers with natural language explanations (2018. 5)
Babble Labble,Data Programming- arXiv, the_morning_blog
- Dropout (2012, 2014)
Regulaizer,Ensemble- arXiv (2012), arXiv (2014), note
- Regularization of Neural Networks using DropConnect (2013)
Regulaizer,Ensemble- paper, note, wanli_summary
- Recurrent Neural Network Regularization (2014. 9)
RNN,Dropout to Non-Recurrent Connections- arXiv
- Batch Normalization (2015. 2)
- Training Very Deep Networks (2015. 7)
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks (2015. 12)
Variational RNN,Dropout - RNN,Bayesian interpretation- arXiv
- Deep Networks with Stochastic Depth (2016. 3)
- Adaptive Computation Time for Recurrent Neural Networks (2016. 3)
ACT,Dynamically,Logic Task- arXiv
- Layer Normalization (2016. 7)
- Recurrent Highway Networks (2016. 7)
- Using Fast Weights to Attend to the Recent Past (2016. 10)
- Professor Forcing: A New Algorithm for Training Recurrent Networks (2016. 10)
- Equality of Opportunity in Supervised Learning (2016. 10)
Equalized Odds,Demographic Parity,Bias- arXiv, the_morning_paper
- Categorical Reparameterization with Gumbel-Softmax (2016. 11)
Gumbel-Softmax distribution,Reparameterization,Smooth relaxation- arXiv, open_review
- Understanding deep learning requires rethinking generalization (2016. 11)
Generalization Error,Role of Regularization- arXiv
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (2017. 1)
- A simple neural network module for relational reasoning (2017. 6)
- On Calibration of Modern Neural Networks (2017. 6)
Confidence calibration,Maximum Calibration Error (MCE)- arXiv
- When is a Convolutional Filter Easy To Learn? (2017. 9)
Conv + ReLU,Non-Gaussian Case,Polynomial Time- arXiv, open_review
- mixup: Beyond Empirical Risk Minimization (2017. 10)
Data Augmentation,Vicinal Risk Minimization,Generalization- arXiv, open_review
- Measuring the tendency of CNNs to Learn Surface Statistical Regularities (2017. 11)
not learn High Level Semantics,learn Surface Statistical Regularities- arXiv, the_morning_paper
- MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels (2017. 12)
MentorNet - StudentNet,Curriculum Learning,Output is Weight- arXiv
- Deep Learning Scaling is Predictable, Empirically (2017. 12)
Power-Law Exponents,Grow Training Sets- arXiv, the_morning_paper
- Sensitivity and Generalization in Neural Networks: an Empirical Study (2018. 2)
Robustness,Data Perturbations,Survey- arXiv, open_review
- Can recurrent neural networks warp time? (2018. 2)
RNN,Learnable Gate,Chrono Initialization- open_review
- Spectral Normalization for Generative Adversarial Networks (2018. 2)
GAN,Training Discriminator,Constrain Lipschitz,Power Method- open_review
- On the importance of single directions for generalization (2018. 3)
Importance,Confusiing Neurons,Selective Neuron,DeepMind- arXiv, deepmind_blog
- Group Normalization (2018. 3)
Group Normalization (GN),Batch (BN),Layer (LN),Instance (IN),Independent Batch Size- arXiv
- Fast Decoding in Sequence Models using Discrete Latent Variables (2018. 3)
Autoregressive,Latent Transformer,Discretization- arXiv
- Delayed Impact of Fair Machine Learning (2018. 3)
Outcome Curve,Max Profit, Demographic Parity, Equal Opportunity- arXiv, the_morning_paper, bair_blog
- How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) (2018. 5)
Smoothing Effect,BatchNorm’s Reparametrization- arXiv
- When Recurrent Models Don't Need To Be Recurrent (2018. 5)
- Relational inductive biases, deep learning, and graph networks (2018, 6)
Survey,Relation,Graph- arXiv
- Universal Transformers (2018. 7)
Transformer,Weight Sharing,Adaptive Computation Time (ACT)- arXiv, google_ai_blog
- Identifying Generalization Properties in Neural Networks (2018. 9)
Generalization,PAC-Bayes,Hessian,Perturbation- arXiv, salesforce_blog
- No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference (2018. 9)
Quantization,Store Multiplication Table,Memory/Power Resources- arXiv
- Distributed Representations of Words and Phrases and their Compositionality (2013. 10)
Word2Vec,CBOW,Skip-gram- arXiv
- GloVe: Global Vectors for Word Representation (2014)
Word2Vec,GloVe,Co-Occurrence- paper
- Convolutional Neural Networks for Sentence Classification (2014. 8)
- Neural Machine Translation by Jointly Learning to Align and Translate (2014. 9)
- Text Understanding from Scratch (2015. 2)
CNN,Character-level- arXiv
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015. 6)
- Pointer Networks (2015. 6)
- Skip-Thought Vectors (2015. 6)
- A Neural Conversational Model (2015. 6)
Seq2Seq,Conversation- arXiv
- Teaching Machines to Read and Comprehend (2015. 6)
- Effective Approaches to Attention-based Neural Machine Translation (2015. 8)
- Character-Aware Neural Language Models (2015. 8)
CNN,Character-level- arXiv
- Neural Machine Translation of Rare Words with Subword Units (2015. 8)
- A Diversity-Promoting Objective Function for Neural Conversation Models (2015. 10)
- Multi-task Sequence to Sequence Learning (2015. 11)
- Multilingual Language Processing From Bytes (2015. 12)
- Strategies for Training Large Vocabulary Neural Language Models (2015. 12)
- Incorporating Structural Alignment Biases into an Attentional Neural Translation Model (2016. 1)
Seq2Seq,Attention with Structural Biases,Translation- arXiv
- Long Short-Term Memory-Networks for Machine Reading (2016. 1)
LSTMN,Intra-Attention,RNN- arXiv
- Recurrent Memory Networks for Language Modeling (2016. 1)
RMN,Memory Bank- arXiv
- Exploring the Limits of Language Modeling (2016. 2)
- Swivel: Improving Embeddings by Noticing What's Missing (2016. 2)
Word2Vec,Swivel,Co-Occurrence- arXiv
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning (2016. 3)
- Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models (2016. 4)
- Adversarial Training Methods for Semi-Supervised Text Classification (2016. 5)
- SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016. 6)
- Sequence-Level Knowledge Distillation (2016. 6)
- Attention-over-Attention Neural Networks for Reading Comprehension (2016. 7)
- Recurrent Neural Machine Translation (2016. 7)
Translation,Attention (RNN)- arXiv
- An Actor-Critic Algorithm for Sequence Prediction (2016. 7)
- Pointer Sentinel Mixture Models (2016. 9)
- Multiplicative LSTM for sequence modelling (2016. 10)
mLSTM,Language Modeling,Character-Level- arXiv
- Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models (2016. 10)
- Fully Character-Level Neural Machine Translation without Explicit Segmentation (2016. 10)
- Neural Machine Translation in Linear Time (2016. 10)
- Bidirectional Attention Flow for Machine Comprehension (2016. 11)
- Dynamic Coattention Networks For Question Answering (2016. 11)
QA,DCN,Coattention Encoder,Machine Comprehension- arXiv
- Dual Learning for Machine Translation (2016. 11)
- Neural Machine Translation with Reconstruction (2016. 11)
- Quasi-Recurrent Neural Networks (2016. 11)
- A recurrent neural network without chaos (2016. 12)
RNN,CFN,Dynamic,Chaos- arXiv
- Comparative Study of CNN and RNN for Natural Language Processing (2017. 2)
Systematic Comparison,CNN vs RNN- arXiv
- A Structured Self-attentive Sentence Embedding (2017. 3)
- Dynamic Word Embeddings for Evolving Semantic Discovery (2017. 3)
Word Embedding,Temporal,Alignment- arXiv, the morning paper
- Learning to Generate Reviews and Discovering Sentiment (2017. 4)
Sentiment,Unsupervised,OpenAI- arXiv
- Ask the Right Questions: Active Question Reformulation with Reinforcement Learning (2017. 5)
QA,Active Question Answering,RL,Agent (Reformulate, Aggregate)- arXiv, open_review
- Reinforced Mnemonic Reader for Machine Reading Comprehension (2017. 5)
QA,Mnemonic (Syntatic, Lexical),RL,Machine Comprehension- arXiv
- Attention Is All You Need (2017. 6)
- Depthwise Separable Convolutions for Neural Machine Translation (2017. 6)
SliceNet,Super-Separable Conv,Depsewise + Conv 1x1- arXiv, open_review, note
- MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension (2017. 7)
MEMEN,QA(MC),Embedding(skip-gram),Full-Orientation Matching- arXiv
- On the State of the Art of Evaluation in Neural Language Models (2017. 7)
Standard LSTM,Regularisation,Hyperparemeter- arXiv
- Text Summarization Techniques: A Brief Survey (2017. 7)
- Adversarial Examples for Evaluating Reading Comprehension Systems (2017. 7)
Concatenative Adversaries(AddSent, AddOneSent),SQuAD- arXiv
- Learned in Translation: Contextualized Word Vectors (2017. 8)
Word Embedding,CoVe,Context Vector- arXiv
- Simple and Effective Multi-Paragraph Reading Comprehension (2017. 10)
- Unsupervised Neural Machine Translation (2017. 10)
Train with both direction (tandem),Shared Encoder,Denoising Auto-Encoder- arXiv, open_review
- Word Translation Without Parallel Data (2017. 10)
Unsupervised,Multilingual Embedding,Parallel Dictionary Induction- arXiv, open_review
- Unsupervised Machine Translation Using Monolingual Corpora Only (2017. 11)
Unsupervised,Adversarial,Monolingual Corpora- arXiv, open_review
- Neural Text Generation: A Practical Guide (2017. 11)
- Breaking the Softmax Bottleneck: A High-Rank RNN Language Model (2017. 11)
MoS (Mixture of Softmaxes),Softmax Bottleneck- arXiv
- Neural Speed Reading via Skim-RNN (2017. 11)
Skim-RNN,Speed Reading,Big(Read)-Small(Skim),Dynamic- arXiv, open_review
- Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks (2017. 11)
SCAN,Compositional,Mix-and-Match- arXiv
- The NarrativeQA Reading Comprehension Challenge (2017. 12)
- Hierarchical Text Generation and Planning for Strategic Dialogue (2017. 12)
End2End Strategic Dialogue,Latent Sentence Representations,Planning + RL- arXiv
- Recent Advances in Recurrent Neural Networks (2018. 1)
RNN,Recent Advances,Review- arXiv
- Personalizing Dialogue Agents: I have a dog, do you have pets too? (2018. 1)
Chit-chat,Profile Memory,Persona-Chat Dataset,ParlAI- arXiv
- Generating Wikipedia by Summarizing Long Sequences (2018. 1)
Multi-Document Summarization,Extractive-Abstractive Stage,T-DMCA,WikiSum,Google Brain- arXiv, note, open_review
- MaskGAN: Better Text Generation via Filling in the______ (2018. 1)
MaskGAN,Neural Text Generation,RL Approach- arXiv, open_review, note
- Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs (2018. 1)
Contextual Decomposition (CD),Disambiguate interactions between Gates- arXiv, open_review
- Universal Language Model Fine-tuning for Text Classification (2018. 1)
ULMFiT,Pre-trained,Transfer Learning- arXiv
- DeepType: Multilingual Entity Linking by Neural Type System Evolution (2018. 2)
DeepType,Symbolic Information,Type System,Open AI- arXiv, openai blog
- Deep contextualized word representations (2018. 2)
- Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018. 2)
Document-Summarization,Cross-Entropy vs RL,Extractive- arXiv
- code2vec: Learning Distributed Representations of Code (2018. 3)
code2vec,Code Embedding,Predicting method name- arXiv
- Universal Sentence Encoder (2018. 3)
Transformer,Deep Averaging Network (DAN),Transfer- arXiv
- An efficient framework for learning sentence representations (2018. 3)
Sentence Representation,True Context,Unsupervised- arXiv, open_review
- An Analysis of Neural Language Modeling at Multiple Scales (2018. 3)
LSTM vs QRNN,Hyperparemeter,AWD-QRNN- arXiv
- Analyzing Uncertainty in Neural Machine Translation (2018. 3)
Uncertainty,Beam Search Degradation,Copy Mode- arXiv
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (2018. 3)
Temporal Convolutional Network (TCN),CNN vs RNN- arXiv
- Training Tips for the Transformer Model (2018. 4)
Transformer,Hyperparameter,Multiple GPU- arXiv
- QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension (2018. 4)
QA,Conv - Self-Attention,Backtranslation (Data Augmentation)- arXiv, open_review, note
- SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach (2018. 4)
Top-K Subject Recognitio,Relation Classification- arXiv
- Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer (2018. 4)
Sentiment Transfer,Disentangle Attribute,Unsupervised- arXiv
- Parsing Tweets into Universal Dependencies (2018. 4)
Universal Dependencies (UD),TWEEBANK v2- arXiv
- Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018. 4)
SR,Subword Sampling + Hyperparameter,Segmentation (BPE, Unigram)- arXiv
- Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (2018. 4)
PI-SQuAD,Challenge,Document Encoder,Scalability- arXiv
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018. 4)
GLUE,Benchmark,Understanding- arXiv, leaderboard
- On the Practical Computational Power of Finite Precision RNNs for Language Recognition (2018. 5)
Unbounded counting,IBFP-LSTM- arXiv
- Paper Abstract Writing through Editing Mechanism (2018. 5)
Writing-editing Network,Attentive Revision Gate- arXiv
- A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (2018. 5)
Unsupervised initialization scheme,Robust self-leraning- arXiv
- Efficient and Robust Question Answering from Minimal Context over Documents (2018. 5)
- Global-Locally Self-Attentive Dialogue State Tracker (2018. 5)
GLAD,WoZ and DSTC2 Dataset- arXiv
- Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information (2018, 5)
Dataset,EVPI,ACL 2018 Best Paper- arXiv
- Know What You Don't Know: Unanswerable Questions for SQuAD (2018, 6)
SQuAD 2.0,Negative Example,ACL 2018 Best Paper- arXiv, leaderboard
- The Natural Language Decathlon: Multitask Learning as Question Answering (2018, 6)
decaNLP,Multitask Question Answering Network (MQAN),Transfer Learning- arXiv
- GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations (2018, 6)
Transfer Learning Framework,Structured Graphical Representations- arXiv
- Improving Language Understanding by Generative Pre-Training (2018, 6)
Transformer,Generative Pre-Training,Discriminative Fine-Tuning- paper, open_ai_blog
- Finding Syntax in Human Encephalography with Beam Search (2018, 6)
RNNG+beam search,ACL 2018 Best Paper- arXiv
- Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers (2018, 6)
Task,Dataset,Weighted-Pooling (WP)ACL 2018 Best Paper- arXiv
- QuAC : Question Answering in Context (2018. 8)
Information-Seeking dialog,Challenge,Without Evidence- arXiv, leaderboard
- CoQA: A Conversational Question Answering Challenge (2018. 8)
Abstractive with Extractive Rationale,Challenge,Coreference and Pragmatic Reasoning- arXiv, leaderboard
- Contextual Parameter Generation for Universal Neural Machine Translation (2018. 8)
Parameter Generation,Language Embedding,EMNLP 2018- arXiv
- Evaluating Theory of Mind in Question Answering (2018. 8)
Dataset,Higher-order Beliefs,EMNLP 2018- arXiv
- Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (2018. 9)
GRAFT-Net,KB+Text Fusion,EMNLP 2018- arXiv
- HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (2018. 9)
Dataset,Multi-hop,Sentence-level Supporting Fact,EMNLP 2018- arXiv
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018. 10)
BERT,Discriminative,Pre-trained,Transfer Learning,NAACL 2019 Best- arXiv
- Trellis Networks for Sequence Modeling (2018. 10)
TrellisNet,Structural bridge between TCN and RNN,NAACL 2019- arXiv
- CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (2018. 11)
CommonsenseQA,Dataset,Multiple-Choice,NAACL 2019 Best- arXiv
- Cross-lingual Language Model Pretraining (2019. 1)
XLM,MLM + TLM,Cross-lingual Pre-trained,Low-Resource- arXiv
- Better Language Models and Their Implications (2019. 2)
- Parameter-Efficient Transfer Learning for NLP (2019. 2)
Adapter tuning,Bottleneck,BERT- arXiv
- To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (2019. 3)
Fine-tuning vs Feature,BERT and ELMo,Empirically analyze- arXiv
- Linguistic Knowledge and Transferability of Contextual Representations (2019. 3)
Analysis CWRs,LSTM, Transformer,Transferable,NAACL 2019- arXiv
- ERNIE: Enhanced Representation through Knowledge Integration (2019. 4)
ERNIE,Masking Strategies,Dialog Language Model,Pre-trained,Transfer Learning- arXiv
- CNM: An Interpretable Complex-valued Network for Matching (2019. 4)
CNM,Quantum Physics,Interpretable,NAACL 2019 Best- arXiv
- Unsupervised Recurrent Neural Network Grammars (2019. 4)
RNNG,Syntax Tree,Variational Inference- arXiv
- The Curious Case of Neural Text Degeneration (2019. 4)
Nucleus Sampling,Decoding Method,Generation- arXiv
- Unified Language Model Pre-training for Natural Language Understanding and Generation (2019. 5)
UniLM,Uni + Bi + S2S,Generation- arXiv
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019. 5)
SuperGLUE,Benchmark,Understanding- arXiv, leaderboard
- SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019. 7)
SpanBERT,Span Boundary Objective (SBO),Pre-train,Transformer- arXiv
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019. 7)
RoBERTa,Data-BatchSize,Pre-train,Transformer- arXiv
- ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (2019. 7)
ERNIE,Continual Pre-training,Word-Struct-Semantic,Transformer- arXiv
- StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019. 8)
StructBERT (ALICE),Language Structure,Pre-train,Transformer- arXiv
- Matching Networks for One Shot Learning (2016. 6)
Matching Nets,Non-Parametric,DeepMind- arXiv, the morning paper
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (2017. 3)
- SMASH: One-Shot Model Architecture Search through HyperNetworks (2017. 8)
SMASH,HyperNet,Prior Knowledge- arXiv, open_review
- Reptile: a Scalable Metalearning Algorithm (2018. 3)
Reptile,Meta-Learning,Few-Shot,OpenAI- arXiv, openai_blog
- Understanding the difficulty of training deep feedforward neural networks (2010)
- On the difficulty of training Recurrent Neural Networks (2012. 11)
Gradient Clipping,RNN- arXiv
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (2015. 2)
- A Simple Way to Initialize Recurrent Networks of Rectified Linear Units (2015. 4)
Weight Initialization,RNN,Identity Matrix- arXiv
- Cyclical Learning Rates for Training Neural Networks (2015. 6)
CLR,Triangular, ExpRange,Longtherm Benefit- arXiv
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima (2016. 9)
Generalization,Sharpness of Minima- arXiv
- Neural Optimizer Search with Reinforcement Learning (2017. 9)
Neural Optimizer Search (NOS),PowerSign,AddSign- arXiv
- On the Convergence of Adam and Beyond (2018. 2)
AMSGrad,Convex optimization- open_review
- Adafactor: Adaptive Learning Rates with Sublinear Memory Cost (2018. 4)
Adafactor,Adaptive Method,Update Clipping- arXiv
- Revisiting Small Batch Training for Deep Neural Networks (2018. 4)
Generalization Performance,Training Stability- arXiv
- Reconciling modern machine learning and the bias-variance trade-off (2018. 12)
Double Descent Risk Curve,Highly Complex Models- arXiv
- Progressive Neural Networks (2016. 6)
ProgNN,Incorporate Prior Knowledge- arXiv, the morning paper
- Neural Architecture Search with Reinforcement Learning (2016. 11)
NAS,Google AutoML,Google Brain- arXiv
- Third-Person Imitation Learning (2017. 3)
Imitation Learning,Unsupervised (Third-Person),GAN + Domain Confusion- arXiv
- Noisy Networks for Exploration (2017. 6)
- Efficient Neural Architecture Search via Parameter Sharing (2018. 2)
ENAS,Google AutoML,Google Brain- arXiv
- Learning by Playing - Solving Sparse Reward Tasks from Scratch (2018. 2)
- Investigating Human Priors for Playing Video Games (2018. 2)
prior knowledge,key factor- open_review
- World Models (2018. 3)
Generative + RL,VAE (V),MDN-RNN (M),Controller (C)- arXiv
- Unsupervised Predictive Memory in a Goal-Directed Agent (2018. 3)
MERLIN,Memory + RL + Inference,Partial Observability- arXiv, google_ai_blog
- ...
- Auto-Encoding Variational Bayes (2013. 12)
- Generative Adversarial Networks (2014. 6)
- Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016. 5)
DVBF,Variational Inference,SVGB- arXiv, open_review
- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient (2016. 9)
- Structured Inference Networks for Nonlinear State Space Models (2016. 9)
Structured Variational Approximation,SVGB- arXiv
- beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (2016. 11)
Beta-VAE,Disentangled- open_review
- A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (2017. 10)
Kalman VAE,LGSSM- arXiv
- Self-Attention Generative Adversarial Networks (2018. 5)
SAGAN,Attention-Driven,Spectral Normalization- arXiv
- Unsupervised Data Augmentation (2019. 4)
UDA,TSA Schedule,Semi-Supervised- arXiv