145 citations · 424 across the 41 of their papers we have counts for
11 papers · 1 filter
Supported Trust Region Optimization for Offline Reinforcement Learning
Yixiu Mao, Hongchang Zhang, Chen Chen +2
Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error. Most policy constraint methods regularize the density of the trained policy towar…
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
Mingxin Li, Richong Zhang, Zhijie Nie +1
Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream t…
3D ScatterNet: Inference from 21 cm Light-cones
Xiaosheng Zhao, Shifan Zuo, Yi Mao
The Square Kilometre Array (SKA) will have the sensitivity to take the 3D light-cones of the 21 cm signal from the epoch of reionization. This signal, however, is highly non-Gaussi…
Evaluating Summary Statistics with Mutual Information for Cosmological Inference
Ce Sui, Xiaosheng Zhao, Tao Jing +1
The ability to compress observational data and accurately estimate physical parameters relies heavily on informative summary statistics. In this paper, we introduce the use of mutu…
Multi-fidelity Emulator for Cosmological Large Scale 21 cm Lightcone Images: a Few-shot Transfer Learning Approach with GAN
Kangning Diao, Yi Mao
Large-scale numerical simulations () of cosmic reionization are required to match the large survey volume of the upcoming Square Kilometre Array (SKA). We pres…
Can Diffusion Model Conditionally Generate Astrophysical Images?
Xiaosheng Zhao, Yuan-Sen Ting, Kangning Diao +1
Generative adversarial networks (GANs) are frequently utilized in astronomy to construct an emulator of numerical simulations. Nevertheless, training GANs can prove to be a precari…
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Junfan Chen, Richong Zhang, Yongyi Mao +1
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets…
Adversarial Defenses via Vector Quantization
Zhiyi Dong, Yongyi Mao
Adversarial attacks pose significant challenges to the robustness of modern deep neural networks in computer vision, and defending these networks against adversarial attacks has at…
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Junfan Chen, Richong Zhang, Zheyan Luo +2
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the succes…
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Kai Sun, Richong Zhang, Samuel Mensah +3
Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expresse…
Tighter Information-Theoretic Generalization Bounds from Supersamples
Ziqiao Wang, Yongyi Mao
In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting of Steinke & Zakynthinou (2020)-the se…