Running Paper
Introduction
Summary
Introduction
ArXiv
2020
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Graph-Bert: Only Attention is Needed for Learning Graph Representations
META-LEARNING INITIALIZATIONS FOR LOW-RESOURCE DRUG DISCOVERY
Transformers are Graph Neural Networks
2019
Max-margin Class Imbalance Learning with Gaussian Affinity
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Rep the Set: Neural Networks for Learning Set Representations
DeeplyTough: Learning Structural Comparison of Protein Binding Sites
SGD on Neural Networks Learns Functions of Increasing Complexity
Brain Signal Classification via Learning Connectivity Structure
Drug-Deug Adverse Effect Prediction with Graph Co-Attention
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Gated Graph Recursive Neural Networks for Molecular Property Prediction
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Focal Loss for Dense Object Detection
Message Passing Graph Kernels
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
Realistic Evaluation of Semi-Supervised Learning Algorithms
Gradient Regularization Improves Accuracy of Discriminate Models
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate
Convergence of Gradient Descent on Separable Data
The Implicit Bias of Gradient Descent on Separable Data
CINIC-10 Is Not ImageNet or CIFAR-10
BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
Theory of Curriculum Learning, with Convex Loss Functions
Adversarial Learning and Explainability in Structured Datasets
Learning to Reweight Examples for Robust Deep Learning
Reconciling modern machine learning and the bias-variance trade-off
Drug repurposing through joint learning on knowledge graphs and literature
On First-Order Meta-Learning Algorithms
2017
Adversarial Learning for Neural Dialogue Generation
Graph Convolutional Encoders for Syntax-aware Neural Machine Network
Multimodal Word Distributions
Practical Neural Network Performance Prediction for Early Stopping
The Marginal Value of Adaptive Gradient Methods in Machine Learning
YellowFin and the Art of Momentum Tuning
Convergence of Deep Neural Networks to a Hierarchical Covariance Matrix Decomposition
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Distral: Robust Multitask Reinforcement Learning
Consistent Multitask Learning with Nonlinear Output Relations
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
A Brief Survey of Deep Reinforcement Learning
An Overview of Multi-Task Learning in Deep Neural Networks
A Study on Neural Network Language Modeling
Learning to Compose Domain-Specific Transformations for Data Augmentation
Autonomous Extracting a Hierarchical Structure of Tasks in reinforcement Learning and Multi-task Rei
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
One pixel attack for fooling deep neural networks
Practical Black-Box Attacks against Machine Learning
Size-Independent Sample Complexity of Neural Networks
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Spectrally-normalized margin bounds for neural networks
A Theory of Feature Learning
StarSpace: Embed All The Things!
Geometry of Optimization and Implicit Regularization in Deep Learning
On The Robustness of a Neural Network
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Towards Proving the Adversarial Robustness of Deep Neural Networks
Modeling Relational Data with Graph Convolutional Networks
2016
automatic chemical design using a data-driven continuous representation of molecules
achieving human parity in conversational speech recognition
Matching Networks for One Shot Learning
Not Just A Black Box: Learning Important Features Through Propagating Activation Differences
Massively Multilingual Word Embeddings
Multi-task Learning with Deep Model Based Reinforcement Learning
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Tutorial on Variational Autoencoders
Equality of Opportunity in Supervised Learning
Train Faster, generalize better, Stability of stochastic gradient descent
2015
Visualizing and Understanding Recurrent Networks
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Character-Aware Neural Language Models
A Theory of Feature Learning
2013
Efficient Estinamtion of Word Representation in Vector Space
2001
Classes for Fast Maximum Entropy Training
Other
2016
Transfer Reinforcement Learning with Shared Dynamics
Improved Graph-based Dependency Parsing via Hierarchical LSTM Networks
Intelligence without representation
Applied Linear Regression
talk
SILO 12.13
Some PAC-Bayesian Theorems
Kernel
Kernel on Bag of Paths for Measuring Similarity of Shapes
Bayesian
Deep Chem
ArXiv
Generating Focussed Molecule Libraries for Drug Discovery with RNN
Boosting Docking-based Virtual Screening with Deep Learning
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule developm
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
Molecular De-Novo Design through Deep Reinforcement Learning
Grammar Variational Autoencoder
Multi-task Neural Networks for QSAR Predictions
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
DeepChem
benefits of depth in neural networks
massively multitask networks for drug discovery
learning classifiers from only positive and unlabeled data
DeepTox: Toxicity Prediction using Deep Learning
The Relationship Between Precision-Recall and ROC Curves
Low Data Drug Discovery with One-shot Learning
Modeling Industrial ADMET Data with Multitask Networks
Deep Learning as an Opportunity in Virtual Screening
Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships
Journal of Medicinal Chemistry
Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay
J Chem Inf Model
2018
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
PotentialNet for Molecular Property Prediction
2017
Is Multitask Deep Learning Practical for Pharma
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
2016
Data-Driven Derivation of an Informer Compound Set for Improved Selection of Active Compounds in Hig
2015
Get Your Atoms in Order - An Open-Source Implementation of a Novel and Robust Molecular Canonicaliza
2014
Implementation of the Hungarian Algorithm to Account for Ligand Symmetry and Similarity in Structure
2009
Influence Relevance Voting: An Accurate Interpretable Virtual High Throughput Screening Method
Bioinformatics
2019
Graph convolutional networks for computational drug development and discovery
2018
Modeling polypharmacy side effects with graph convolutional networks
2017
Deepsite: protein-binding site predictor using 3D-convolutional neural network
2010
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval
Journal of Proteome Research
Optimization of Statistical Methods Impact on Quantitative Proteomics Data
ML & DL
ICML
2020
Composing Molecules with Multiple Property Constraints
Hierarchical Generation of Molecular Graphs using Structural Motifs
2019
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Bridging Theory and Algorithm for Domain Adaptation
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
GMNN: Graph Markov Neural Networks
Optimal Transport for structured data with application on graphs
Learning Discrete Structures for Graph Neural Networks
Learning What and Where to Transfer
Domain Agnostic Learning with Disentangled Representations
Position-aware Graph Neural Networks
2018
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
To Understand Deep Learning We Need to Understand Kernel Learning
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
TuringBox: An Experimental Platform for the Evaluation of AI Systems
Learning Adversarially Fair and Transferable Representations
Learning to Reweight Examples for Robust Deep Learning
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Hierarchical Multi-Label Classification Networks
Mutual Information Neural Estimation
2017
End-to-End Learning for Structured Prediction Energy Networks
Grammar Variational Autoencoder
Multi-task Learning with Labeled and Unlabeled Tasks
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
A Closer Look at Memorization in Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Understanding Black-box Predictions via Influence Functions
On the Expressive Power of Deep Neural Networks
Neural Episodic Control
Risk Bounds for Transferring Representations With and Without Fine-Tuning
Learning Algorithm for Active Learning
Neural Message Passing for Quantum Chemistry
Axiomatic Attribution for Deep Networks
Meta Networks
2016
Structured Prediction Energy Networks
Learning Relational Sum-Product Networks
Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
Tutorial: Deep Reinforcement Learning
Meta-Learning with Memory-Augmented Neural Networks
Learning Convolutional Neural Networks for Graphs
Generalization Properties and Implicit Regularization for Multiple Passes SGM
2015
From Word Embeddings To Document Distances
Siamese Neural Networks for One-shot Image Recognition
2014
Distributed Representations of Sentences and Documents
Large-scale Multi-label Learning with Missing Labels
Deterministic Policy Gradient Algorithms
A PAC-Bayesian Bound for Lifelong Learning
2012
Learning Task Grouping and Overlap in Multi-Task Learning
2011
Learning with Whom to Share in Multi-task Feature Learning
Bayesian Learning via Stochastic Gradient Langevin Dynamics
2006
NIPS
2019
Graph Structured Prediction Energy Networks
Wasserstein Weisfeiler-Lehman Graph Kernels
GOT: An Optimal Transport framework for Graph comparison
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Graph Normalizing Flows
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Learning interpretable disease self-representations for drug repositioning
GENERATIVE MODELS FOR GRAPH-BASED PROTEIN DESIGN
Implicit Generation and Modeling with Energy-Based Models
2018
Convolutional Set Matching for Graph Similarity
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Generalized Cross Entropy for Training Deep Neural Networks with Noisy Labels
Multi-Layered Gradient Boosting Decision Trees
Constrained Graph Variational Autoencoders for Molecule Design
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Deep Defense: Training DNNs with Improved Adversarial Robustness
KONG: Kernels for ordered-neighborhood graphs
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Multi-Task Learning as Multi-Objective Optimization
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Neural Ordinary Differential Equations
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Multi-Task Graph Autoencoders
Pre-training Graph Neural Networks with Kernels
2017
Dynamic Routing Between Capsules
Learning Multiple Tasks with Multilinear Relationship Networks
Exploring Generalization in Deep Learning
Protein Interface Prediction using Graph Convolutional Networks
PAC-Bayesian Generalization Bound for Multi-class Learning
A Unified Approach to Interpreting Model Predictions
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
Implicit Regularization in Matrix Factorization
Deep Sets
Masked Autoregressive Flow for Density Estimation
Attention Is All You Need
Prototypical Networks for Few-shot Learning
2016
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Network
Value Iteration Networks
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning
Multi-task Learning for Predicting Health, Stress, and Happiness
Learning to learn by gradient descent by gradient descent
Active One-shot Learning
Matching Networks for One Shot Learning
Variational Graph Auto-Encoders
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
2015
Introduction to Reinforcement Learning with Function Approximation
Recent Advances in Post-Selection Statistical Inference
Convolutional Networks on Graphs for Learning Molecular Fingerprints
2014
Generative Adversarial Nets
Communication Efficient Distributed Machine Learning with the Parameter Server
Recurrent Models of Visual Attention
Neural Word Embedding as Implicit Matrix Factorization
Semi-supervised Learning with Deep Generative Models
2013
Parameter Server for Distributed Machine Learning
Multi-Task Bayesian Optimization
Distributed Representations of Words and Phrases and their Compositionality
Playing Atari with Deep Reinforcement Learning
2012
Practical Bayesian Optimization of Machine Learning Algorithms
2010
Self-Paced Learning for Latent Variable Models
2009
A Scalable Hierarchical Distributed Language Model
2003
Distance metric learning, with application to clustering with side-information
2002
An Impossibility Theorem for Clustering
nips2019
1993
Signature Verification using a "Siamese" Time Delay Neural Network
ICLR
2020
NEURAL EXECUTION OF GRAPH ALGORITHMS
DEEP GRAPH MATCHING CONSENSUS
DIRECTIONAL MESSAGE PASSING FOR MOLECULAR GRAPHS
A FAIR COMPARISON OF GRAPH NEURAL NETWORKS FOR GRAPH CLASSIFICATION
GENERATING VALID EUCLIDEAN DISTANCE MATRICES
GraphAF: A Flow-Based Autoregressive Model for Molecular Graph Generation
Understanding and Improving Information Transfer in Multi-Task Learning
RAPID LEARNING OR FEATURE REUSE? TOWARDS UNDERSTANDING THE EFFECTIVENESS OF MAML
Strategies for Pre-Training Graph Neural Networks
2019
Cross-Entropy Loss Leads To Poor Margins
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Robustness May Be at Odds with Accuracy
How Powerful Are Graph Neural Networks?
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
CGNF: Conditional Graph Neural Fields
Learning Deep Representations By Mutual Information Estimation and Maximization
Learning Protein Structure with a Differentiable Simulator
2018
Deep Gradient Compression: Reducing The Communication Bandwidth For Distributed Training
Don't Decay The Learning Rate, Increase The Batch Size
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
Learning to cluster in order to transfer across domains and tasks
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Covariant Compositional Networks for Learning Graphs
The Implicit Bias of Gradient Descent on Separable Data
Towards Deep Learning Models Resistant to Adversarial Attacks
Few-Shot Learning with Graph Neural Networks
2017
A Simple But Tough-to-beat Baseline For Sentence Embeddings
Optimization as a Model For Few-Shot Learning
Towards Principled Methods For Training Generative Adversarial Networks
Multi-Agent Cooperation And The Emergence of Natural Language
Understanding Deep Learning Requires Rethinking Generalization
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Learning To Optimize
Diet Networks: Thin Parameters for Fat Genomics
Semi-Supervised Classification with Graph Convolutional Networks
Density Estimation Using Real NVP
A Structured Self-Attentive Sentence Embedding
2016
Segmental Recurrent Neural Networks
Order Matters: Sequence To Sequence For Sets
Learning to SMILE(S)
Multi-task Sequence To Sequence Learning
Actor-Mimic Deep Multitask and Transfer Reinforcement Learning
All You Need is A Good Init
ORDER MATTERS: SEQUENCE TO SEQUENCE FOR SETS
2015
Adam: A Method For Stochastic Optimization
A Unified Perspective on Multi-Domain And Multi-Task Learning
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Explaining and Harnessing Adversarial Examples
2014
Auto-Encoding Variational Bayes
Nature
2019
Network-based prediction of drug combinations
AAAI
2019
Gradient Harmonized Single-stage Detector
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
2018
Adaptive Graph Convolutional Neural Networks
Learning with Incomplete Labels
2017
Deep MIML Network
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Self-Paced Multi-Task Learning
2016
SVVAMP: Simulator of Various Voting Algorithms in Manipulating Populations
IJCAI
2018
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
2017
Self-Paced Multitask Learning with Shared Knowledge
Online Multitask Relative Similarity Learning
AISTAT
2019
Towards Optimal Transport with Global Invariances
2017
Less Than a Single Pass: Stochastically Controlled Stochastic Gradient
Communication-Efficient Learning of Deep Networks from Decentralized Data
2007
Semi-Supervised Mean Fields
2005
Hierarchical Probabilistic Neural Network Language Model
Scalable ML
2016
SGDKDD
2019
Retaining Privileged Information for Multi-Task Learning
2017
Algorithmic decision making and the cost of fairness
2016
"Why Should I Trust You?" Explaining the Predictions of Any Classifier
2015
Pentuum: A New Platform for Distributed Machine Learning on Big Data
Heterogeneous Network Embedding via Deep Architectures
WSDM
2017
Recurrent Recommender Networks
Comparative Document Analysis for Large Text Corpora
Summarizing Answers in Non-Factoid Community Question-Answering
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
CVPR
2019
Class-Imbalanced Loss Based on Effective Number of Samples
2018
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Learning to Compare: Relation Network for Few-Shot Learning
2017
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classifi
Scene Graph Generation by Iterative Message Passing
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
2016
Deep Residual Learning for Image Recognition
Learning Deep Representation for Imbalanced Classification
2015
Curriculum Learning of Multiple Tasks
Going Deeper with Convolutions
2014
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers
ICCV
2017
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach
Makeup-Go: Blind Reversion of Protrait Edit
2015
Deep Learning Face Attributes in the Wild
IEEE
2016
Efficient Distributed Estimation of Inverse Covariance Matrices
Asynchrony begets Momentum, with an Application to Deep Learning
1993
Universal Approximation Bounds for Superpositions of a Sigmoidal Function
FAT/ML
2018
2017
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
ITCS
2017
Inherent Trade-Offs in the Fair Determination of Risk Scores
2012
Fairness Through Awareness
JMLR
2011
Weisfeiler-Lehman Graph Kernels
IEEE
2018
Imbalanced Deep Learning by Minority Class Incremental Rectification
NLP
ACL
2017
Deep Multitask Learning for Semantic Dependency Parsing
2016
Summarizing Source Code using a Neural Attention Model
Document-level Sentiment Inference with Social, Faction, and Discourse Context
EMNLP
2018
One-Shot Relational Learning for Knowledge Graphs
2017
Learning to select data for transfer learning with Bayesian Optimization
Supervised Learning of Universal Sentence Representations from Natrual Language Inference Data
2016
cached long short-term memory neural networks for document-level sentiment classification
Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser
Analyzing Framing through the Casts of Characters in the News
2015
Exploring Markov Logic Networks for Question Answering
EMNLP versus ACL: Analyzing NLP Research Over Time
WIKIQA: A Challenge Dataset for Open-Domain Question
NAACL
2018
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
2015
EACL
2017
What Do Recurrent Neural Network Grammers Learn About Syntax?
Identifying benefitial task relations for mulit-task learning in deep neural network
ISCA
isca_2016
Segmental Recurrent Neural Network for End-to-end Speech Recognition
Big Data System
OSDI
2016
TensorFlow: A system for large-scale machine learning
2014
Scaling Distributed Machine Learning with the Parameter Server
GraphX: Graph Processing in a Distributed Dataflow Framework
NSDI
2016
StreamScope: Continuous Reliable Distributed Processing of Big Data Streams
2015
Global analytics in the face of bandwidth and regulatory constraints
Succint: Enabling Queries on Compressed Data
2012
Resilient Distributed Datasets: A Fault-Tolerance Abstraction for In-Memory Cluster Computing
2011
Donimant Resource Fairness: Fair Allocation of Multiple Resource Types
SIGMOD
2015
Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications
2014
Storm @Twitter
2010
Pregel: A System for Large-Scale Graph Processing
SOSP
2013
Naiad: A Timely Dataflow System
Discretized Streams: Fault-Tolerance Streaming Computation at Scale
Eurosys
2013
BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data
Powered by
GitBook
Covariant Compositional Networks for Learning Graphs
Covariant Compositional Networks for Learning Graphs
Covariant Compositional Networks for Learning Graphs
Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi
UChicago, TTIC
Intro
results matching "
"
No results matching "
"