1 code implementation • COLING 2022 • Xiao Ding, Bowen Chen, Li Du, Bing Qin, Ting Liu
To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.
1 code implementation • EMNLP 2021 • Jihao Shi, Xiao Ding, Li Du, Ting Liu, Bing Qin
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.
no code implementations • 20 May 2023 • Li Du, Hongyuan Mei, Jason Eisner
To predict the next token, autoregressive models ordinarily examine the past.
no code implementations • 20 Dec 2022 • Li Du, Lucas Torroba Hennigen, Tiago Pimentel, Clara Meister, Jason Eisner, Ryan Cotterell
Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings.
1 code implementation • 16 Dec 2022 • Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Bing Qin, Yi Zheng, Baoxing Huai
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
no code implementations • 6 Dec 2022 • Lirui Xiao, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang
CSQ stabilizes the bit-level mixed-precision training process with a bi-level gradual continuous sparsification on both the bit values of the quantized weights and the bit selection in determining the quantization precision of each layer.
no code implementations • 1 Dec 2022 • Jianing Li, Ming Lu, Jiaming Liu, Yandong Guo, Li Du, Shanghang Zhang
In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner.
no code implementations • CVPR 2023 • Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang
Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer.
no code implementations • 18 Oct 2022 • Shuo Xie, Jiahao Qiu, Ankita Pasad, Li Du, Qing Qu, Hongyuan Mei
We propose to select layers based on the variability of their hidden states given a task-specific corpus.
no code implementations • 26 Aug 2022 • Jianing Li, Jiaming Liu, Xiaobao Wei, Jiyuan Zhang, Ming Lu, Lei Ma, Li Du, Tiejun Huang, Shanghang Zhang
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
no code implementations • 21 Aug 2022 • Tingting Wu, Xiao Ding, Hao Zhang, Jinglong Gao, Li Du, Bing Qin, Ting Liu
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
no code implementations • 14 Aug 2022 • Bowen Chen, Xiao Ding, Li Du, Qin Bing, Ting Liu
Given a task, human learns from easy to hard, whereas the model learns randomly.
no code implementations • Findings (ACL) 2022 • Li Du, Xiao Ding, Yue Zhang, Kai Xiong, Ting Liu, Bing Qin
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
1 code implementation • ACL 2022 • Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
no code implementations • 8 Feb 2022 • Guhong Nie, Lirui Xiao, Menglong Zhu, Dongliang Chu, Yue Shen, Peng Li, Kang Yang, Li Du, Bo Chen
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.
no code implementations • 12 Oct 2021 • Zhuang Shao, Xiaoliang Chen, Li Du, Lei Chen, Yuan Du, Wei Zhuang, Huadong Wei, Chenjia Xie, Zhongfeng Wang
To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps.
1 code implementation • ACL 2021 • Li Du, Xiao Ding, Ting Liu, Bing Qin
Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.
1 code implementation • ACL 2021 • Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.
no code implementations • IJCNLP 2019 • Li Du, Xiao Ding, Ting Liu, Zhongyang Li
Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP).
no code implementations • 6 Sep 2019 • Jinming Lu, Siyuan Lu, Zhisheng Wang, Chao Fang, Jun Lin, Zhongfeng Wang, Li Du
With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations.
15 code implementations • ICLR 2020 • Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi
Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators.
no code implementations • 19 Sep 2017 • Yuan Du, Li Du, Xuefeng Gu, Jieqiong Du, X. Shawn Wang, Boyu Hu, Mingzhe Jiang, Xiaoliang Chen, Junjie Su, Subramanian S. Iyer, Mau-Chung Frank Chang
The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces.
no code implementations • 15 Sep 2017 • Yuan Du, Li Du, Yilei Li, Junjie Su, Mau-Chung Frank Chang
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.
no code implementations • 8 Jul 2017 • Li Du, Yuan Du, Yilei Li, Mau-Chung Frank Chang
To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed.