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09/13/2023
GPU Scheduler for De Novo Genome Assembly with Multiple MPI Processes
De Novo Genome assembly is one of the most important tasks in computatio...
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08/22/2023
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Reinforcement learning serves as a potent tool for modeling dynamic user...
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04/17/2023
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
Recommendation models are typically trained on observational user intera...
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04/17/2023
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Reinforcement learning-based recommender systems have recently gained po...
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03/03/2023
Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches
Pipeline parallelism has been demonstrated to be a remarkable approach t...
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02/16/2023
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
We present Rhino, a system for accelerating tensor programs with automat...
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02/13/2023
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
This paper presents TAG, an automatic system to derive optimized DNN tra...
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09/26/2022
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
This paper proposes DisCo, an automatic deep learning compilation module...
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09/17/2022
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
Deep reinforcement learning (DRL) has been proven its efficiency in capt...
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08/10/2022
Deep Reinforcement Learning for Dynamic Recommendation with Model-agnostic Counterfactual Policy Synthesis
Recent advances in recommender systems have proved the potential of Rein...
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06/17/2022
Fast Lossless Neural Compression with Integer-Only Discrete Flows
By applying entropy codecs with learned data distributions, neural compr...
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04/01/2022
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Interactive recommendation is able to learn from the interactive process...
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12/02/2021
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Adversarial attacks, e.g., adversarial perturbations of the input and ad...
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07/08/2020
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
The last decade has witnessed growth in the computational requirements f...
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07/02/2020
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
It is a challenging task to train large DNN models on sophisticated GPU ...
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06/01/2020
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
This paper presents our modeling and architecture approaches for buildin...
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04/02/2019