Search Results for author: Tianyi Zhang

Found 91 papers, 46 papers with code

Splitting vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation

no code implementations ECCV 2020 Tianyi Zhang, Guosheng Lin, Weide Liu, Jianfei Cai, Alex Kot

Finally, by training the segmentation model with the masks generated by our Splitting vs Merging strategy, we achieve the state-of-the-art weakly-supervised segmentation results on the Pascal VOC 2012 benchmark.

Segmentation Weakly supervised segmentation +2

NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention

no code implementations2 Mar 2024 Tianyi Zhang, Jonah Wonkyu Yi, Bowen Yao, Zhaozhuo Xu, Anshumali Shrivastava

Large language model inference on Central Processing Units (CPU) is challenging due to the vast quantities of expensive Multiply-Add (MAD) matrix operations in the attention computations.

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

1 code implementation29 Feb 2024 Tianyi Zhang, Yu Cao, Dianbo Liu

Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos.

Federated Learning

Customizable Perturbation Synthesis for Robust SLAM Benchmarking

1 code implementation12 Feb 2024 Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Xiaonan Huang

To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.

Benchmarking Simultaneous Localization and Mapping

Learning Scalable Structural Representations for Link Prediction with Bloom Signatures

no code implementations28 Dec 2023 Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava

We further show that any type of neighborhood overlap-based heuristic can be estimated by a neural network that takes Bloom signatures as input.

Link Prediction

Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation

1 code implementation27 Dec 2023 Xiawei Li, Qingyuan Xu, Jing Zhang, Tianyi Zhang, Qian Yu, Lu Sheng, Dong Xu

The point affinity proposed in this paper is characterized by features from multiple modalities (e. g., point cloud and RGB), and is further refined by normalizing the classifier weights to alleviate the detrimental effects of long-tailed distribution without the need of the prior of category distribution.

3D Semantic Segmentation Point Cloud Segmentation +1

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision

no code implementations26 Dec 2023 Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, Xuanmao Li, Xingpeng Sun, Rohan Ashok, Aniruddha Mukherjee, Hao Kang, Xiangrui Kong, Gang Hua, Tianyi Zhang, Bedrich Benes, Aniket Bera

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS).

Novel View Synthesis Representation Learning

Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis

no code implementations14 Dec 2023 Yafei Hu, Quanting Xie, Vidhi Jain, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Shibo Zhao, Yu Quan Chong, Chen Wang, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Zsolt Kira, Fei Xia, Yonatan Bisk

Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.

Compositional Inversion for Stable Diffusion Models

1 code implementation13 Dec 2023 Xulu Zhang, Xiao-Yong Wei, Jinlin Wu, Tianyi Zhang, Zhaoxiang Zhang, Zhen Lei, Qing Li

It stems from the fact that during inversion, the irrelevant semantics in the user images are also encoded, forcing the inverted concepts to occupy locations far from the core distribution in the embedding space.

Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation

no code implementations12 Dec 2023 Shaopeng Zhai, Jie Wang, Tianyi Zhang, Fuxian Huang, Qi Zhang, Ming Zhou, Jing Hou, Yu Qiao, Yu Liu

Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks.

Decision Making Language Modelling +1

Transformer-based Selective Super-Resolution for Efficient Image Refinement

no code implementations10 Dec 2023 Tianyi Zhang, Kishore Kasichainula, Yaoxin Zhuo, Baoxin Li, Jae-sun Seo, Yu Cao

Conventional super-resolution methods suffer from two drawbacks: substantial computational cost in upscaling an entire large image, and the introduction of extraneous or potentially detrimental information for downstream computer vision tasks during the refinement of the background.

Super-Resolution

PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles

1 code implementation12 Nov 2023 Tianyi Zhang, Shangqing Lyu, Yanli Lei, Sicheng Chen, Nan Ying, Yufang He, Yu Zhao, Yunlu Feng, Hwee Kuan Lee, Guanglei Zhang

Firstly, we identify three task focuses that can effectively bridge knowledge of pathological and natural domain: appearance consistency, spatial consistency, and restoration understanding.

Self-Supervised Learning

Privacy-preserving design of graph neural networks with applications to vertical federated learning

no code implementations31 Oct 2023 Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).

Graph Representation Learning Management +2

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

1 code implementation27 Oct 2023 Nan Ying, Yanli Lei, Tianyi Zhang, Shangqing Lyu, Chunhui Li, Sicheng Chen, Zeyu Liu, Yu Zhao, Guanglei Zhang

This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.

Self-Supervised Learning Transfer Learning +1

Self-supervision meets kernel graph neural models: From architecture to augmentations

no code implementations17 Oct 2023 Jiawang Dan, Ruofan Wu, Yunpeng Liu, Baokun Wang, Changhua Meng, Tengfei Liu, Tianyi Zhang, Ningtao Wang, Xing Fu, Qi Li, Weiqiang Wang

Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).

Data Augmentation Graph Classification +2

Point-Based Radiance Fields for Controllable Human Motion Synthesis

1 code implementation5 Oct 2023 HaiTao Yu, Deheng Zhang, Peiyuan Xie, Tianyi Zhang

This paper proposes a novel controllable human motion synthesis method for fine-level deformation based on static point-based radiance fields.

Motion Synthesis Novel View Synthesis

Learning Orbitally Stable Systems for Diagrammatically Teaching

no code implementations19 Sep 2023 Weiming Zhi, Kangni Liu, Tianyi Zhang, Matthew Johnson-Roberson

In this work, we tackle the problem of teaching a robot to approach a surface and then follow cyclic motion on it, where the cycle of the motion can be arbitrarily specified by a single user-provided sketch over an image from the robot's camera.

MORPH

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks

no code implementations18 Sep 2023 Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, Weiqiang Wang

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.

Reasoning about the Unseen for Efficient Outdoor Object Navigation

1 code implementation18 Sep 2023 Quanting Xie, Tianyi Zhang, Kedi Xu, Matthew Johnson-Roberson, Yonatan Bisk

We introduce a new task OUTDOOR, a new mechanism for Large Language Models (LLMs) to accurately hallucinate possible futures, and a new computationally aware success metric for pushing research forward in this more complex domain.

Navigate Object

Learning from Demonstration via Probabilistic Diagrammatic Teaching

no code implementations7 Sep 2023 Weiming Zhi, Tianyi Zhang, Matthew Johnson-Roberson

Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space.

A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions

no code implementations25 Aug 2023 Tianyi Zhang, Zheng Wang, Jing Huang, Mohiuddin Muhammad Tasnim, Wei Shi

Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework.

Image Generation

Software Entity Recognition with Noise-Robust Learning

1 code implementation21 Aug 2023 Tai Nguyen, Yifeng Di, Joohan Lee, Muhao Chen, Tianyi Zhang

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation.

Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions

no code implementations4 Aug 2023 Samia Kabir, David N. Udo-Imeh, Bonan Kou, Tianyi Zhang

Despite this popularity, no comprehensive study has been conducted to evaluate the characteristics of ChatGPT's answers to programming questions.

Misinformation

PharmacyGPT: The AI Pharmacist

no code implementations19 Jul 2023 Zhengliang Liu, Zihao Wu, Mengxuan Hu, Bokai Zhao, Lin Zhao, Tianyi Zhang, Haixing Dai, Xianyan Chen, Ye Shen, Sheng Li, Brian Murray, Tianming Liu, Andrea Sikora

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.

Rapid Image Labeling via Neuro-Symbolic Learning

1 code implementation18 Jun 2023 Yifeng Wang, Zhi Tu, Yiwen Xiang, Shiyuan Zhou, Xiyuan Chen, Bingxuan Li, Tianyi Zhang

To address this challenge, we propose a neuro-symbolic approach called Rapid, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules.

Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation

no code implementations2 Jun 2023 Bonan Kou, Shengmai Chen, Zhijie Wang, Lei Ma, Tianyi Zhang

Through a quantitative experiment and a user study, we confirmed that, among twelve different attention computation methods, attention computed by the perturbation-based method is most aligned with human attention and is constantly favored by human programmers.

Code Generation

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

2 code implementations NeurIPS 2023 Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto

As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003.

Instruction Following

Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations

1 code implementation12 May 2023 Yuan Tian, Zheng Zhang, Zheng Ning, Toby Jia-Jun Li, Jonathan K. Kummerfeld, Tianyi Zhang

Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries.

Text-To-SQL

Evaluating Verifiability in Generative Search Engines

1 code implementation19 Apr 2023 Nelson F. Liu, Tianyi Zhang, Percy Liang

Generative search engines directly generate responses to user queries, along with in-line citations.

Sentence

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

no code implementations18 Apr 2023 Jiasheng Xu, Tianyi Zhang, Yangqian Wu, Jie Yang, Guang-Zhong Yang, Yun Gu

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases.

Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery

1 code implementation6 Apr 2023 Tianyi Zhang, Matthew Johnson-Roberson

The proposed technique integrates underwater light effects into a volume rendering framework with end-to-end differentiability.

DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction

1 code implementation2 Mar 2023 Zhijie Wang, Yuheng Huang, Da Song, Lei Ma, Tianyi Zhang

The core of DeepSeer is a state abstraction method that bundles semantically similar hidden states in an RNN model and abstracts the model as a finite state machine.

Explainable Artificial Intelligence (XAI)

DeepLens: Interactive Out-of-distribution Data Detection in NLP Models

1 code implementation2 Mar 2023 Da Song, Zhijie Wang, Yuheng Huang, Lei Ma, Tianyi Zhang

In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora.

Text Clustering

FairPy: A Toolkit for Evaluation of Social Biases and their Mitigation in Large Language Models

1 code implementation10 Feb 2023 Hrishikesh Viswanath, Tianyi Zhang

and also present a toolkit that provides plug-and-play interfaces to connect mathematical tools to identify biases with large pretrained language models such as BERT, GPT-2 etc.

GRANDE: a neural model over directed multigraphs with application to anti-money laundering

no code implementations4 Feb 2023 Ruofan Wu, Boqun Ma, Hong Jin, Wenlong Zhao, Weiqiang Wang, Tianyi Zhang

The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently.

Edge Classification Graph Representation Learning +1

Benchmarking Large Language Models for News Summarization

1 code implementation31 Jan 2023 Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto

Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood.

Benchmarking News Summarization

LADIS: Language Disentanglement for 3D Shape Editing

1 code implementation9 Dec 2022 IAn Huang, Panos Achlioptas, Tianyi Zhang, Sergey Tulyakov, Minhyuk Sung, Leonidas Guibas

Additionally, to measure edit locality, we define a new metric that we call part-wise edit precision.

Disentanglement

Coder Reviewer Reranking for Code Generation

1 code implementation29 Nov 2022 Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I. Wang

Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.

Code Generation Language Modelling

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

1 code implementation18 Nov 2022 Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu

We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.

Code Generation Memorization

Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification

1 code implementation14 Aug 2022 Tianyi Zhang, Youdan Feng, Yunlu Feng, Yu Zhao, Yanli Lei, Nan Ying, Zhiling Yan, Yufang He, Guanglei Zhang

The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images.

Image Classification

Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization

1 code implementation4 Jul 2022 Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama

Recent years have witnessed a great success of supervised deep learning, where predictive models were trained from a large amount of fully labeled data.

Multi-class Classification

Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances

no code implementations7 Jun 2022 Tianyi Zhang, Youdan Feng, Yunlu Feng, Guanglei Zhang

Computer-aided diagnosis (CAD) using the deep learning method has the potential to solve the problem of insufficient pathology staffing.

Image Classification Multiple Instance Learning

Decentralized Training of Foundation Models in Heterogeneous Environments

1 code implementation2 Jun 2022 Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Re, Ce Zhang

Our key technical contribution is a scheduling algorithm that allocates different computational "tasklets" in the training of foundation models to a group of decentralized GPU devices connected by a slow heterogeneous network.

Scheduling

TempLM: Distilling Language Models into Template-Based Generators

1 code implementation23 May 2022 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B. Hashimoto

While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content.

Text Generation

Model-Based Neural Network and Its Application to Line Spectral Estimation

no code implementations14 Feb 2022 Yi Jiang, Tianyi Zhang, Wei zhang

Owing to the same layered form as an ANN, a MNN can also be optimized using the back-propagation (BP) algorithm.

Rethinking Importance Weighting for Transfer Learning

no code implementations19 Dec 2021 Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama

A key assumption in supervised learning is that training and test data follow the same probability distribution.

Selection bias Test +1

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

no code implementations10 Dec 2021 Tianyi Zhang, Shirui Zhang, Ziwei Chen, Dianbo Liu

Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays.

Federated Learning Meta-Learning +1

On the Opportunities and Risks of Foundation Models

2 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers

1 code implementation16 Jul 2021 Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten

In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.

Graph Attention

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

no code implementations CVPR 2021 Tianyi Zhang, Jie Lin, Peng Hu, Bin Zhao, Mohamed M. Sabry Aly

Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines.

object-detection Object Detection

On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

1 code implementation NAACL 2021 Tianyi Zhang, Tatsunori Hashimoto

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains.

Inductive Bias Language Modelling +1

Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX

no code implementations21 Mar 2021 Jiaying Ren, Tianyi Zhang, Jian Li, Petre Stoica

In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function.

Computational Efficiency

Joint RFI Mitigation and Radar Echo Recovery for One-Bit UWB Radar

no code implementations19 Mar 2021 Tianyi Zhang, Jiaying Ren, Jian Li, Lam H. Nguyen, Petre Stoica

Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques.

Learning to Stop with Surprisingly Few Samples

no code implementations19 Feb 2021 Daniel Russo, Assaf Zeevi, Tianyi Zhang

We consider a discounted infinite horizon optimal stopping problem.

RFI Mitigation for One-bit UWB Radar Systems

no code implementations17 Feb 2021 Tianyi Zhang, Jiaying Ren, Jian Li, Lam H. Nguyen, Petre Stoica

A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology.

Computational Efficiency Quantization

Can Steering Wheel Detect Your Driving Fatigue?

no code implementations18 Oct 2020 Jianchao Lu, Xi Zheng, Tianyi Zhang, Michael Sheng, Chen Wang, Jiong Jin, Shui Yu, Wanlei Zhou

In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel.

ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses

no code implementations22 Aug 2020 Min Fu, Jiwei Guan, Xi Zheng, Jie zhou, Jianchao Lu, Tianyi Zhang, Shoujie Zhuo, Lijun Zhan, Jian Yang

Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers.

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

no code implementations8 Aug 2020 Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He

In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.

Fraud Detection Management

A One-step Approach to Covariate Shift Adaptation

no code implementations8 Jul 2020 Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.

Test

Revisiting Few-sample BERT Fine-tuning

1 code implementation ICLR 2021 Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, Yoav Artzi

We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.

Demystifying Orthogonal Monte Carlo and Beyond

no code implementations NeurIPS 2020 Han Lin, Haoxian Chen, Tianyi Zhang, Clement Laroche, Krzysztof Choromanski

Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction.

Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

no code implementations19 Mar 2020 Tianyi Zhang, Yun Gu, Xiaolin Huang, Enmei Tu, Jie Yang

In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules.

Image Super-Resolution

Supporting OpenMP 5.0 Tasks in hpxMP -- A study of an OpenMP implementation within Task Based Runtime Systems

1 code implementation19 Feb 2020 Tianyi Zhang, Shahrzad Shirzad, Bibek Wagle, Adrian S. Lemoine, Patrick Diehl, Hartmut Kaiser

This paper is a follow-up paper on the fundamental implementation of hpxMP, an implementation of the OpenMP standard which utilizes the C++ standard library for Parallelism and Concurrency (HPX) to schedule and manage tasks.

Distributed, Parallel, and Cluster Computing Programming Languages

An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models

1 code implementation6 Feb 2020 Yao Deng, Xi Zheng, Tianyi Zhang, Chen Chen, Guannan Lou, Miryung Kim

We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e. g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.

Autonomous Driving

Identifying Mislabeled Data using the Area Under the Margin Ranking

2 code implementations NeurIPS 2020 Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger

Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled.

Test

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach

no code implementations20 Oct 2019 Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama

The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors.

Classification General Classification

QPyTorch: A Low-Precision Arithmetic Simulation Framework

2 code implementations9 Oct 2019 Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa

Low-precision training reduces computational cost and produces efficient models.

Quantization

Detecting Noisy Training Data with Loss Curves

no code implementations25 Sep 2019 Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger

This paper introduces a new method to discover mislabeled training samples and to mitigate their impact on the training process of deep networks.

Fixed-price Diffusion Mechanism Design

no code implementations14 May 2019 Tianyi Zhang, Dengji Zhao, Wen Zhang, Xuming He

We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially.

SWALP : Stochastic Weight Averaging in Low-Precision Training

2 code implementations26 Apr 2019 Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa

Low precision operations can provide scalability, memory savings, portability, and energy efficiency.

An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System

1 code implementation7 Mar 2019 Tianyi Zhang, Shahrzad Shirzad, Patrick Diehl, R. Tohid, Weile Wei, Hartmut Kaiser

Not only must users port their own codes, but often users rely on highly optimized libraries such as BLAS and LAPACK which use OpenMP for parallization.

Distributed, Parallel, and Cluster Computing

Simplifying Graph Convolutional Networks

7 code implementations19 Feb 2019 Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

Graph Regression Image Classification +5

Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference

no code implementations29 Mar 2018 Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li

In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with the variance-reduced Langevin dynamics.

Bayesian Inference

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