b'Wei Lin'

research

∙ 09/20/2023

DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

We propose DISC-LawLLM, an intelligent legal system utilizing large lang...

0 Shengbin Yue, et al. ∙

research

∙ 09/19/2023

Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity

With the fast growth of parameter size, it becomes increasingly challeng...

0 Haojun Xia, et al. ∙

research

∙ 09/13/2023

CARE: Large Precision Matrix Estimation for Compositional Data

High-dimensional compositional data are prevalent in many applications. ...

0 Shucong Zhang, et al. ∙

research

∙ 09/13/2023

TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification

Vision and Language Models (VLMs), such as CLIP, have enabled visual rec...

0 M. Jehanzeb Mirza, et al. ∙

research

∙ 06/23/2023

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Multimodal Large Language Model (MLLM) relies on the powerful LLM to per...

0 Chaoyou Fu, et al. ∙

research

∙ 06/07/2023

Multivariate two-sample test statistics based on data depth

Data depth has been applied as a nonparametric measurement for ranking m...

0 Yiting Chen, et al. ∙

research

∙ 05/29/2023

LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections

Recently, large-scale pre-trained Vision and Language (VL) models have s...

0 M. Jehanzeb Mirza, et al. ∙

research

∙ 05/11/2023

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhi...

0 Haoran Luo, et al. ∙

research

∙ 04/11/2023

Neural Delay Differential Equations: System Reconstruction and Image Classification

Neural Ordinary Differential Equations (NODEs), a framework of continuou...

0 Qunxi Zhu, et al. ∙

research

∙ 03/27/2023

AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection

Unsupervised domain adaptive object detection is a challenging vision ta...

0 Kunyang Sun, et al. ∙

research

∙ 03/16/2023

Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays

Reservoir computing (RC), a particular form of recurrent neural network,...

0 Xing-Yue Duan, et al. ∙

research

∙ 03/15/2023

MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge

Large scale Vision-Language (VL) models have shown tremendous success in...

0 Wei Lin, et al. ∙

research

∙ 03/09/2023

TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering

Temporal action segmentation in untrimmed videos has gained increased at...

0 Wei Lin, et al. ∙

research

∙ 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...

0 Siyu Wang, et al. ∙

research

∙ 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...

0 Shiwei Zhang, et al. ∙

research

∙ 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...

0 Shiwei Zhang, et al. ∙

research

∙ 02/01/2023

TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation

Model parallelism has become necessary to train large neural networks. H...

0 Ziji Shi, et al. ∙

research

∙ 11/26/2022

SKDBERT: Compressing BERT via Stochastic Knowledge Distillation

In this paper, we propose Stochastic Knowledge Distillation (SKD) to obt...

0 Zixiang Ding, et al. ∙

research

∙ 11/24/2022

Video Test-Time Adaptation for Action Recognition

Although action recognition systems can achieve top performance when eva...

0 Wei Lin, et al. ∙

research

∙ 11/23/2022

ActMAD: Activation Matching to Align Distributions for Test-Time-Training

Test-Time-Training (TTT) is an approach to cope with out-of-distribution...

0 Muhammad Jehanzeb Mirza, et al. ∙

research

∙ 11/21/2022

MATE: Masked Autoencoders are Online 3D Test-Time Learners

We propose MATE, the first Test-Time-Training (TTT) method designed for ...

0 M. Jehanzeb Mirza, et al. ∙

research

∙ 10/07/2022

Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering

We present a neural point cloud rendering pipeline through a novel multi...

29 Jay Karhade, et al. ∙

research

∙ 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...

0 Xiaodong Yi, et al. ∙

research

∙ 09/19/2022

Heterogeneous Federated Learning on a Graph

Federated learning, where algorithms are trained across multiple decentr...

9 Huiyuan Wang, et al. ∙

research

∙ 08/30/2022

EasyScale: Accuracy-consistent Elastic Training for Deep Learning

Distributed synchronized GPU training is commonly used for deep learning...

0 Mingzhen Li, et al. ∙

research

∙ 06/17/2022

GDsmith: Detecting Bugs in Graph Database Engines

Graph database engines stand out in the era of big data for their effici...

0 Wei Lin, et al. ∙

research

∙ 05/03/2022

Cross-View Cross-Scene Multi-View Crowd Counting

Multi-view crowd counting has been previously proposed to utilize multi-...

11 Qi Zhang, et al. ∙

research

∙ 04/30/2022

EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing

The success of Pre-Trained Models (PTMs) has reshaped the development of...

25 Chengyu Wang, et al. ∙

research

∙ 04/22/2022

Efficient Pipeline Planning for Expedited Distributed DNN Training

To train modern large DNN models, pipeline parallelism has recently emer...

0 Ziyue Luo, et al. ∙

research

∙ 04/11/2022

PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

The development of personalized recommendation has significantly improve...

0 Yuanxing Zhang, et al. ∙

research

∙ 03/30/2022

CycDA: Unsupervised Cycle Domain Adaptation from Image to Video

Although action recognition has achieved impressive results over recent ...

5 Wei Lin, et al. ∙

research

∙ 01/04/2022

Neural Piecewise-Constant Delay Differential Equations

Continuous-depth neural networks, such as the Neural Ordinary Differenti...

8 Qunxi Zhu, et al. ∙

research

∙ 10/22/2021

Temporal Point Process Graphical Models

Many real-world objects can be modeled as a stream of events on the node...

0 Yalong Lyu, et al. ∙

research

∙ 10/08/2021

M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

Recent expeditious developments in deep learning algorithms, distributed...

7 Junyang Lin, et al. ∙

research

∙ 09/10/2021

Principal component analysis for high-dimensional compositional data

Dimension reduction for high-dimensional compositional data plays an imp...

0 Jingru Zhang, et al. ∙

research

∙ 08/24/2021

Binary Code based Hash Embedding for Web-scale Applications

Nowadays, deep learning models are widely adopted in web-scale applicati...

0 Bencheng Yan, et al. ∙

research

∙ 08/24/2021

Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer

Embedding learning for categorical features is crucial for the deep lear...

0 Bencheng Yan, et al. ∙

research

∙ 07/16/2021

Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data

Recently, neural network compression schemes like channel pruning have b...

9 Jiandong Mu, et al. ∙

research

∙ 06/09/2021

Harmless Overparametrization in Two-layer Neural Networks

Overparametrized neural networks, where the number of active parameters ...

0 Huiyuan Wang, et al. ∙

research

∙ 05/31/2021

Exploring Sparse Expert Models and Beyond

Mixture-of-Experts (MoE) models can achieve promising results with outra...

10 An Yang, et al. ∙

research

∙ 05/17/2021

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach

In real-world search, recommendation, and advertising systems, the multi...

0 Xu Ma, et al. ∙

research

∙ 03/09/2021

DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Many recent machine learning models show dynamic shape characteristics. ...

0 Kai Zhu, et al. ∙

research

∙ 03/01/2021

M6: A Chinese Multimodal Pretrainer

In this work, we construct the largest dataset for multimodal pretrainin...

14 Junyang Lin, et al. ∙

research

∙ 02/22/2021

Neural Delay Differential Equations

Neural Ordinary Differential Equations (NODEs), a framework of continuou...

16 Qunxi Zhu, et al. ∙

research

∙ 12/16/2020

Focusing More on Conflicts with Mis-Predictions Helps Language Pre-Training

In this work, we propose to improve the effectiveness of language pre-tr...

4 Chen Xing, et al. ∙

research

∙ 11/18/2020

EasyTransfer – A Simple and Scalable Deep Transfer Learning Platform for NLP Applications

The literature has witnessed the success of applying deep Transfer Learn...

63 Minghui Qiu, et al. ∙

research

∙ 11/18/2020

Whale: A Unified Distributed Training Framework

Data parallelism (DP) has been a common practice to speed up the trainin...

0 Ang Wang, et al. ∙

research

∙ 10/28/2020

INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices

The intensive computation of Automatic Speech Recognition (ASR) models o...

0 Yiwu Yao, et al. ∙

research

∙ 09/23/2020

FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads

We show in this work that memory intensive computations can result in se...

0 Zhen Zheng, et al. ∙

research

∙ 09/09/2020

One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction

Structured information extraction from document images usually consists ...

21 Mengli Cheng, et al. ∙

Wei Lin

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