1 code implementation • 28 Feb 2025 • Pengyu Zhang, Xieyuanli Chen, Yuwei Chen, Beizhen Bi, Zhuo Xu, Tian Jin, Xiaotao Huang, Liang Shen
Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle.
no code implementations • 17 Feb 2025 • Tian Jin, Ellie Y. Cheng, Zack Ankner, Nikunj Saunshi, Blake M. Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin
We present PASTA, a learning-based system that teaches LLMs to identify semantic independence and express parallel decoding opportunities in their own responses.
no code implementations • 5 Feb 2025 • Wenhao Wang, Zijie Yu, William Liu, Rui Ye, Tian Jin, Siheng Chen, Yanfeng Wang
To tackle these challenges, we propose FedMobileAgent, a collaborative framework that trains mobile agents using self-sourced data from diverse users.
no code implementations • 21 Jan 2025 • Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan Alistarh, Gintare Karolina Dziugaite
Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large language models (LLMs).
no code implementations • 15 Oct 2024 • Seth Lazar, Luke Thorburn, Tian Jin, Luca Belli
Our information and communication environment has fallen short of the ideals that networked global communication might have served.
no code implementations • 19 Jun 2024 • Chengyao Tang, Yongpeng Dai, Zhi Li, Tian Jin
This paper presents two new, simple yet effective approaches to measure the vibration of a swaying millimeter-wave radar (mmRadar) utilizing geometrical information.
no code implementations • 20 Apr 2024 • Chengyao Tang, Yongpeng Dai, Zhi Li, Yongping Song, Fulai Liang, Tian Jin
Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring.
no code implementations • 14 Apr 2024 • Tian Jin, Wanzin Yazar, Zifei Xu, Sayeh Sharify, Xin Wang
We demonstrate that using this custom CUDA kernel improves the throughput of LLM inference by 28%.
2 code implementations • 15 Nov 2023 • William Brandon, Aniruddha Nrusimha, Kevin Qian, Zachary Ankner, Tian Jin, Zhiye Song, Jonathan Ragan-Kelley
In experiments running Striped Attention on A100 GPUs and TPUv4s, we are able to achieve up to 1. 45x end-to-end throughput improvements over the original Ring Attention algorithm on causal transformer training at a sequence length of 256k.
no code implementations • 7 Oct 2023 • Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
We study two natural scaling techniques -- weight pruning and simply training a smaller or larger model, which we refer to as dense scaling -- and their effects on two core capabilities of LLMs: (a) recalling facts presented during pre-training and (b) processing information presented in-context during inference.
no code implementations • 1 Dec 2022 • Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin
Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum fraction of weights that pruning can remove without compromising the model's test accuracy.
no code implementations • 25 Oct 2022 • Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
Pruning models in this over-parameterized regime leads to a contradiction -- while theory predicts that reducing model size harms generalization, pruning to a range of sparsities nonetheless improves it.
1 code implementation • 19 Aug 2020 • Tian Jin, Gheorghe-Teodor Bercea, Tung D. Le, Tong Chen, Gong Su, Haruki Imai, Yasushi Negishi, Anh Leu, Kevin O'Brien, Kiyokuni Kawachiya, Alexandre E. Eichenberger
Deep neural network models are becoming increasingly popular and have been used in various tasks such as computer vision, speech recognition, and natural language processing.
no code implementations • ACL 2020 • Tian Jin, Zhun Liu, Shengjia Yan, Alex Eichenberger, re, Louis-Philippe Morency
In this paper, we propose \textbf{N3} (\textbf{N}eural \textbf{N}etworks from \textbf{N}atural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model.
no code implementations • 26 Oct 2019 • Liang Shen, Jiahua zhu, Chongyi Fan, Xiaotao Huang, Tian Jin
In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching.
no code implementations • Neurocomputing 2019 • Hao Du, Tian Jin, Yuan He, Yongping Song, Yongpeng Dai
In this work, we propose a neural network architecture, namely segmented convolutional gated recurrent neural network (SCGRNN), to recognize human activities based on micro-Doppler spectrograms measured by the ultra-wideband radar.
no code implementations • 9 Feb 2019 • Chu Qin, Ying Tan, Shang Ying Chen, Xian Zeng, Xingxing Qi, Tian Jin, Huan Shi, Yiwei Wan, Yu Chen, Jingfeng Li, Weidong He, Yali Wang, Peng Zhang, Feng Zhu, Hongping Zhao, Yuyang Jiang, Yuzong Chen
We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1. 39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space.