84 citations · 181 across the 12 of their papers we have counts for
12 papers
Learning from Ambiguous Data with Hard Labels
Zeke Xie, Zheng He, Nan Lu +5
Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores. Standard training using ambiguous data with these hard labels may…
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing
Ye Ma, Mingming Sun, Ping Li
Recent years have witnessed the impressive progress in Neural Dependency Parsing. According to the different factorization approaches to the graph joint probabilities, existing par…
On the Overlooked Structure of Stochastic Gradients
Zeke Xie, Qian-Yuan Tang, Mingming Sun +1
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs). Some works attempted to explain the success of stochastic optimization f…
CGAR: Critic Guided Action Redistribution in Reinforcement Leaning
Tairan Huang, Xu Li, Hao Li +2
Training a game-playing reinforcement learning agent requires multiple interactions with the environment. Ignorant random exploration may cause a waste of time and resources. It's…
Dataset Pruning: Reducing Training Data by Examining Generalization Influence
Shuo Yang, Zeke Xie, Hanyu Peng +3
The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial q…
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space
Jinxing Yu, Yunfeng Cai, Mingming Sun +1
Translation distance based knowledge graph embedding (KGE) methods, such as TransE and RotatE, model the relation in knowledge graphs as translation or rotation in the vector space…
Under-confidence Backdoors Are Resilient and Stealthy Backdoors
Minlong Peng, Zidi Xiong, Quang H. Nguyen +3
By injecting a small number of poisoned samples into the training set, backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designe…
On the Power-Law Hessian Spectrums in Deep Learning
Zeke Xie, Qian-Yuan Tang, Yunfeng Cai +2
It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the…
S-MLPv2: Improved Spatial-Shift MLP Architecture for Vision
Tan Yu, Xu Li, Yunfeng Cai +2
Recently, MLP-based vision backbones emerge. MLP-based vision architectures with less inductive bias achieve competitive performance in image recognition compared with CNNs and vis…
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
Tan Yu, Xu Li, Yunfeng Cai +2
In the past decade, we have witnessed rapid progress in the machine vision backbone. By introducing the inductive bias from the image processing, convolution neural network (CNN) h…
S-MLP: Spatial-Shift MLP Architecture for Vision
Tan Yu, Xu Li, Yunfeng Cai +2
Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNN…
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
Weijie Zhao, Deping Xie, Ronglai Jia +4
Neural networks of ads systems usually take input from multiple resources, e.g., query-ad relevance, ad features and user portraits. These inputs are encoded into one-hot or multi-…