89 citations · 167 across the 10 of their papers we have counts for
10 papers
: A "Spot the Difference" Challenge for Large Multimodal Models
Kewei Wei, Bocheng Hu, Jie Cao +13
Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend…
A Comprehensive Study on the Use of Word Embedding Models in Software Engineering Domain
Xiaohan Chen, Weiqin Zou, Lianyi Zhi +2
Word embedding (WE) techniques are advanced textual semantic representation models oriented from the natural language processing (NLP) area. Inspired by their effectiveness in faci…
Chasing Better Deep Image Priors between Over- and Under-parameterization
Qiming Wu, Xiaohan Chen, Yifan Jiang +1
Deep Neural Networks (DNNs) are well-known to act as over-parameterized deep image priors (DIP) that regularize various image inverse problems. Meanwhile, researchers also proposed…
Towards Constituting Mathematical Structures for Learning to Optimize
Jialin Liu, Xiaohan Chen, Zhangyang Wang +2
Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A g…
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to…
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Shiwei Liu, Tianlong Chen, Xiaohan Chen +4
Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this p…
Hyperparameter Tuning is All You Need for LISTA
Xiaohan Chen, Jialin Liu, Zhangyang Wang +1
Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces the concept of unrolling an iterative algorithm and training it like a neural network. It has had great succes…
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Xiaolong Ma, Geng Yuan, Xuan Shen +8
There have been long-standing controversies and inconsistencies over the experiment setup and criteria for identifying the "winning ticket" in literature. To reconcile such, we rev…
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi +6
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning l…
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and…