31 citations · 51 across the 10 of their papers we have counts for
10 papers
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation
Can Chen, Hao Liu, Zeming Liu +2
Proactively and naturally guiding the dialog from the non-recommendation context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is crucial for the Conversational Re…
Ternary Quantization: A Survey
Dan Liu, Xue Liu
Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to compress neural network models with fast…
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can Chen, Yingxue Zhang, Xue Liu +1
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence d…
Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning
Dan Liu, Xue Liu
Most existing pruning works are resource-intensive, requiring retraining or fine-tuning of the pruned models for accuracy. We propose a retraining-free pruning method based on hype…
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu, Xing Tang, Hong Zhu +4
Learning embedding table plays a fundamental role in Click-through rate(CTR) prediction from the view of the model performance and memory usage. The embedding table is a two-dimens…
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Can Chen, Yingxue Zhang, Jie Fu +2
In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting aris…
Structure-aware Protein Self-supervised Learning
Can Chen, Jingbo Zhou, Fan Wang +2
Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on protein classification. Moreover, a few r…
Generalized Data Weighting via Class-level Gradient Manipulation
Can Chen, Shuhao Zheng, Xi Chen +4
Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a…
Learning Multi-Objective Curricula for Robotic Policy Learning
Jikun Kang, Miao Liu, Abhinav Gupta +3
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designe…
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
Fuyuan Lyu, Xing Tang, Huifeng Guo +4
Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and ite…