Le Song
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Le Songcomputer-science Degrees
Computer Science
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Algorithms
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Machine Learning
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Artificial Intelligence
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Computer Science
Le Song's Degrees
- PhD Computer Science Carnegie Mellon University
- Masters Computer Science Carnegie Mellon University
- Bachelors Computer Science Tsinghua University
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Why Is Le Song Influential?
(Suggest an Edit or Addition)Le Song's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- SphereFace: Deep Hypersphere Embedding for Face Recognition (2017) (2177)
- Learning Combinatorial Optimization Algorithms over Graphs (2017) (949)
- Discriminative Embeddings of Latent Variable Models for Structured Data (2016) (561)
- Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (2016) (512)
- Adversarial Attack on Graph Structured Data (2018) (501)
- GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016) (471)
- Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (2017) (406)
- Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (2018) (384)
- Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes (2013) (343)
- Variational Reasoning for Question Answering with Knowledge Graph (2017) (300)
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017) (298)
- Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013) (266)
- Syntax-Directed Variational Autoencoder for Structured Data (2018) (257)
- Deep Fried Convnets (2014) (246)
- Learning to Branch in Mixed Integer Programming (2016) (243)
- Iterative Learning with Open-set Noisy Labels (2018) (238)
- SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (2017) (221)
- Learning Triggering Kernels for Multi-dimensional Hawkes Processes (2013) (216)
- Scalable Kernel Methods via Doubly Stochastic Gradients (2014) (213)
- Heterogeneous Graph Neural Networks for Malicious Account Detection (2018) (210)
- GeniePath: Graph Neural Networks with Adaptive Receptive Paths (2018) (207)
- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution (2015) (204)
- Material structure-property linkages using three-dimensional convolutional neural networks (2018) (178)
- Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams (2015) (166)
- Learning Networks of Heterogeneous Influence (2012) (158)
- L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data (2018) (149)
- Shaping Social Activity by Incentivizing Users (2014) (136)
- Fake News Mitigation via Point Process Based Intervention (2017) (136)
- Learning Steady-States of Iterative Algorithms over Graphs (2018) (133)
- Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs (2020) (127)
- Diverse Neural Network Learns True Target Functions (2016) (126)
- Wasserstein Learning of Deep Generative Point Process Models (2017) (124)
- A la Carte - Learning Fast Kernels (2014) (122)
- Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (2018) (121)
- Time-Sensitive Recommendation From Recurrent User Activities (2015) (120)
- Learning from Conditional Distributions via Dual Embeddings (2016) (118)
- Deep Hyperspherical Learning (2017) (112)
- Iterative Machine Teaching (2017) (111)
- Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (2014) (111)
- Learning towards Minimum Hyperspherical Energy (2018) (108)
- Retrosynthesis Prediction with Conditional Graph Logic Network (2020) (102)
- Uncover Topic-Sensitive Information Diffusion Networks (2013) (95)
- Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression (2015) (87)
- Influence Estimation and Maximization in Continuous-Time Diffusion Networks (2016) (87)
- Constructing Disease Network and Temporal Progression Model via Context-Sensitive Hawkes Process (2015) (84)
- Influence Function Learning in Information Diffusion Networks (2014) (83)
- Learning Loop Invariants for Program Verification (2018) (81)
- M-Statistic for Kernel Change-Point Detection (2015) (81)
- Learning Temporal Point Processes via Reinforcement Learning (2018) (80)
- Stochastic Generative Hashing (2017) (79)
- Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades (2015) (78)
- Efficient Probabilistic Logic Reasoning with Graph Neural Networks (2020) (74)
- Deep Coevolutionary Network: Embedding User and Item Features for Recommendation (2016) (73)
- RNA Secondary Structure Prediction By Learning Unrolled Algorithms (2020) (64)
- On the Complexity of Learning Neural Networks (2017) (57)
- Isotonic Hawkes Processes (2016) (56)
- Learning Time Series Associated Event Sequences With Recurrent Point Process Networks (2019) (55)
- Provable Bayesian Inference via Particle Mirror Descent (2015) (54)
- Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions (2016) (53)
- Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search (2020) (49)
- Question Directed Graph Attention Network for Numerical Reasoning over Text (2020) (49)
- Multistage Campaigning in Social Networks (2016) (45)
- Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation (2016) (45)
- Nonparametric Estimation of Multi-View Latent Variable Models (2013) (43)
- Universal machine learning for topology optimization (2021) (43)
- Towards Black-box Iterative Machine Teaching (2017) (43)
- Decoupled Networks (2018) (43)
- PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks (2019) (42)
- Boosting the Actor with Dual Critic (2017) (42)
- Double Neural Counterfactual Regret Minimization (2018) (42)
- Poly(A) motif prediction using spectral latent features from human DNA sequences (2013) (42)
- Learn to Explain Efficiently via Neural Logic Inductive Learning (2019) (41)
- Learning Conditional Generative Models for Temporal Point Processes (2018) (41)
- Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks (2017) (41)
- Exponential Family Estimation via Adversarial Dynamics Embedding (2019) (41)
- Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape (2017) (40)
- Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients (2015) (39)
- Smart Broadcasting: Do You Want to be Seen? (2016) (37)
- Graph Neural Networks (2021) (37)
- Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning (2019) (35)
- NetCodec: Community Detection from Individual Activities (2015) (35)
- DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding (2020) (35)
- Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees (2019) (35)
- Recurrent Hidden Semi-Markov Model (2016) (35)
- Diversity Leads to Generalization in Neural Networks (2016) (34)
- Communication Efficient Distributed Kernel Principal Component Analysis (2015) (34)
- Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders (2020) (33)
- Distilling Information Reliability and Source Trustworthiness from Digital Traces (2016) (32)
- Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm (2016) (31)
- Learning to Stop While Learning to Predict (2020) (31)
- Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks (2016) (30)
- Learning a Meta-Solver for Syntax-Guided Program Synthesis (2018) (29)
- KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings (2018) (29)
- Meta Architecture Search (2018) (29)
- Syntax-Directed Variational Autoencoder for Molecule Generation (2017) (29)
- Scan B-statistic for kernel change-point detection (2015) (29)
- Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (2020) (29)
- Kernel Exponential Family Estimation via Doubly Dual Embedding (2018) (28)
- Detecting Changes in Dynamic Events Over Networks (2017) (27)
- Least Squares Revisited: Scalable Approaches for Multi-class Prediction (2013) (27)
- Smoothed Dual Embedding Control (2017) (25)
- Orthogonal Over-Parameterized Training (2020) (25)
- Regularizing Neural Networks via Minimizing Hyperspherical Energy (2019) (24)
- GLAD: Learning Sparse Graph Recovery (2019) (24)
- Coupled Variational Bayes via Optimization Embedding (2018) (24)
- Linking Micro Event History to Macro Prediction in Point Process Models (2017) (24)
- Variational Policy for Guiding Point Processes (2017) (23)
- Bandit Samplers for Training Graph Neural Networks (2020) (22)
- Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method (2015) (22)
- Neural Similarity Learning (2019) (21)
- Learning from Conditional Distributions via Dual Kernel Embeddings (2016) (20)
- Robust Low Rank Kernel Embeddings of Multivariate Distributions (2013) (20)
- A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop (2016) (17)
- ProTo: Program-Guided Transformer for Program-Guided Tasks (2021) (16)
- Unfolding Latent Tree Structures using 4th Order Tensors (2012) (16)
- Molecule Optimization by Explainable Evolution (2021) (16)
- Code2Inv: A Deep Learning Framework for Program Verification (2020) (16)
- Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs (2017) (15)
- Deep Semi-Random Features for Nonlinear Function Approximation (2017) (15)
- Distributed Kernel Principal Component Analysis (2015) (14)
- Predicting User Activity Level In Point Processes With Mass Transport Equation (2017) (14)
- Fast and Simple Optimization for Poisson Likelihood Models (2016) (14)
- Cost-Effective Incentive Allocation via Structured Counterfactual Inference (2019) (14)
- Steering Opinion Dynamics in Information Diffusion Networks (2016) (13)
- Budgeted Influence Maximization for Multiple Products (2013) (13)
- Recurrent Coevolutionary Feature Embedding Processes for Recommendation (2017) (13)
- Can Graph Neural Networks Help Logic Reasoning? (2019) (12)
- Structure2vec: Deep Learning for Security Analytics over Graphs (2018) (11)
- The Nonparametric Kernel Bayes Smoother (2016) (10)
- Answering Any-hop Open-domain Questions with Iterative Document Reranking (2020) (10)
- Neural Model-Based Reinforcement Learning for Recommendation (2018) (10)
- TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning (2021) (10)
- Intention Propagation for Multi-agent Reinforcement Learning (2020) (10)
- Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods (2015) (9)
- Learning Continuous-Time Hidden Markov Models for Event Data (2017) (9)
- GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data (2020) (9)
- Detecting weak changes in dynamic events over networks (2016) (9)
- Learning to Plan via Neural Exploration-Exploitation Trees (2019) (9)
- A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media (2015) (8)
- GNN is a Counter? Revisiting GNN for Question Answering (2021) (8)
- Understanding Deep Architecture with Reasoning Layer (2020) (8)
- Co-evolutionary Dynamics of Information Diffusion and Network Structure (2015) (8)
- Reinforcement Learning for Uplift Modeling (2018) (7)
- The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models (2020) (6)
- Continuous-Time Influence Maximization for Multiple Items (2013) (6)
- DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering (2020) (6)
- Scalable Bayesian Inference via Particle Mirror Descent (2015) (6)
- Particle Flow Bayes' Rule (2019) (6)
- ARBITRAR: User-Guided API Misuse Detection (2021) (6)
- Learning Time-Varying Coverage Functions (2014) (5)
- Active Learning and Best-Response Dynamics (2014) (5)
- RoMA: Robust Model Adaptation for Offline Model-based Optimization (2021) (5)
- Point Process Estimation with Mirror Prox Algorithms (2019) (4)
- HEALTHCARE REPRESENTATION LEARNING (2017) (4)
- Temporal Logic Point Processes (2020) (4)
- Learning From Networks: Algorithms, Theory, and Applications (2019) (4)
- A Stochastic Differential Equation Framework for Guiding Information Diffusion (2016) (4)
- Concentric Spherical GNN for 3D Representation Learning (2021) (4)
- POSTER: Neural Network-based Graph Embedding for Malicious Accounts Detection (2017) (4)
- Understanding Deep Architectures with Reasoning Layer (2020) (4)
- Multi-scale Nystrom Method (2018) (3)
- Compressive Hyperspherical Energy Minimization (2019) (3)
- Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients (2021) (3)
- Large-Scale Gaussian Process Regression via Doubly Stochastic Gradient Descent (2015) (3)
- Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem (2014) (3)
- Diffusion in Social and Information Metworks: Research Problems; Probabilistic Models & Machine Learning Methods (2015) (3)
- Meta Particle Flow for Sequential Bayesian Inference (2019) (2)
- Latent Dirichlet Allocation for Internet Price War (2018) (2)
- Multi-task Learning of Order-Consistent Causal Graphs (2021) (2)
- Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits (2016) (2)
- A Framework For Differentiable Discovery Of Graph Algorithms (2021) (2)
- Efficient Dynamic Graph Representation Learning at Scale (2021) (2)
- Language Modeling with Shared Grammar (2019) (1)
- Efficient CFR for Imperfect Information Games with Instant Updates (2019) (1)
- Learning to Optimize via Wasserstein Deep Inverse Optimal Control (2018) (1)
- Exponential Family Estimation via Dynamics Embedding (2018) (1)
- A Unifying Framework for Guiding Point Processes with Stochastic Intensity Functions (2017) (1)
- Learning Temporal Rules from Noisy Timeseries Data (2022) (1)
- Bayesian Meta-network Architecture Learning (2018) (1)
- How to Design Sample and Computationally Efficient VQA Models (2021) (1)
- A Policy Gradient Method with Variance Reduction for Uplift Modeling (2018) (1)
- Online Supervised Subspace Tracking (2015) (0)
- OOSTING THE A CTOR WITH D UAL C RITIC (2018) (0)
- Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling (2021) (0)
- Regularizing Neural Networks via Minimizing Hyperspherical Energy (with Appendix) (2020) (0)
- Gene expression Sequence 2 Vec : a novel embedding approach for modeling transcription factor binding affinity landscape (2017) (0)
- Deep Interaction Processes for Time-Evolving Graphs (2019) (0)
- Solving the Linear Bellman Equation via Dual Kernel Embeddings (2017) (0)
- Session details: DAEN 2015 (2015) (0)
- Recurrent Temporal Point Process (2016) (0)
- Decoupled Networks ( with Appendix ) (2018) (0)
- Appendix A Proof Details A . 1 Proof of Theorem 3 Theorem (0)
- Supplementary Material For Kdd2017: Efficient And Accurate Materials Structure-Property Linkages Using 3D Convolutional Neural Networks (2017) (0)
- Diverse Neural Network Learns True Target Functions A Spherical harmonic decomposition and kernel spectrum (2017) (0)
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What Schools Are Affiliated With Le Song?
Le Song is affiliated with the following schools:
