Search Results for author: Min Li

Found 38 papers, 9 papers with code

DeepSeq: Deep Sequential Circuit Learning

no code implementations27 Feb 2023 Sadaf Khan, Zhengyuan Shi, Min Li, Qiang Xu

Circuit representation learning is a promising research direction in the electronic design automation (EDA) field.

Representation Learning

TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training

1 code implementation20 Feb 2023 Chang Chen, Min Li, Zhihua Wu, dianhai yu, Chao Yang

In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology.

Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

no code implementations7 Nov 2022 Zhengkun Tian, Hongyu Xiang, Min Li, Feifei Lin, Ke Ding, Guanglu Wan

To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients.

Knowledge Distillation

Coded Residual Transform for Generalizable Deep Metric Learning

no code implementations9 Oct 2022 Shichao Kan, Yixiong Liang, Min Li, Yigang Cen, Jianxin Wang, Zhihai He

To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability.

Metric Learning

Fast geometric trim fitting using partial incremental sorting and accumulation

no code implementations5 Sep 2022 Min Li, Laurent Kneip

We apply our method to two distinct camera resectioning algorithms, and demonstrate highly efficient and reliable, geometric trim fitting.


SATformer: Transformers for SAT Solving

no code implementations2 Sep 2022 Zhengyuan Shi, Min Li, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, Qiang Xu

In this paper, we propose SATformer, a novel Transformer-based solution for Boolean satisfiability (SAT) solving.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

no code implementations6 Aug 2022 Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.

Transfer Learning

PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation

1 code implementation2 Jul 2022 Huimin Zhu, Renyi Zhou, Jing Tang, Min Li

The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets.

Drug Discovery

DeepTPI: Test Point Insertion with Deep Reinforcement Learning

1 code implementation7 Jun 2022 Zhengyuan Shi, Min Li, Sadaf Khan, Liuzheng Wang, Naixing Wang, Yu Huang, Qiang Xu

Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement.

Q-Learning reinforcement-learning +1

DeepSAT: An EDA-Driven Learning Framework for SAT

no code implementations27 May 2022 Min Li, Zhengyuan Shi, Qiuxia Lai, Sadaf Khan, Shaowei Cai, Qiang Xu

Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling.

RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations

no code implementations29 Apr 2022 Shu-Mei Qin, Min Li, Tao Xu, Shao-Qun Dong

This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions.

DeepGate: Learning Neural Representations of Logic Gates

1 code implementation26 Nov 2021 Min Li, Sadaf Khan, Zhengyuan Shi, Naixing Wang, Yu Huang, Qiang Xu

We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate.

Representation Learning

Testability-Aware Low Power Controller Design with Evolutionary Learning

1 code implementation26 Nov 2021 Min Li, Zhengyuan Shi, Zezhong Wang, Weiwei Zhang, Yu Huang, Qiang Xu

The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.

T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis

no code implementations ICLR 2022 Minhao Liu, Ailing Zeng, Qiuxia Lai, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu

In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.

Activity Recognition Representation Learning +2

Rethinking the Misalignment Problem in Dense Object Detection

1 code implementation27 Aug 2021 Yang Yang, Min Li, Bo Meng, Junxing Ren, Degang Sun, Zihao Huang

On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.

Dense Object Detection object-detection +1

A Pseudo Label-wise Attention Network for Automatic ICD Coding

no code implementations12 Jun 2021 Yifan Wu, Min Zeng, Ying Yu, Min Li

The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes.

Multi-Label Classification Pseudo Label

Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

no code implementations30 May 2021 Xiongshi Deng, Min Li, Shaobo Deng, Lei Wang

In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm.

RFCBF: enhance the performance and stability of Fast Correlation-Based Filter

no code implementations30 May 2021 Xiongshi Deng, Min Li, Lei Wang, Qikang Wan

Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining.

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks

no code implementations NeurIPS 2021 Yu Li, Min Li, Qiuxia Lai, Yannan Liu, Qiang Xu

To be specific, we first build a similarity graph on test instances and training samples, and we conduct graph-based semi-supervised learning to extract contextual features.

Image Classification

AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference

no code implementations10 May 2021 Min Li, Yu Li, Ye Tian, Li Jiang, Qiang Xu

This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions.

Image Classification

Objects as Extreme Points

no code implementations29 Apr 2021 Yang Yang, Min Li, Bo Meng, Zihao Huang, Junxing Ren, Degang Sun

We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss.

object-detection Object Detection

Skimming and Scanning for Untrimmed Video Action Recognition

no code implementations21 Apr 2021 Yunyan Hong, Ailing Zeng, Min Li, Cewu Lu, Li Jiang, Qiang Xu

Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes.

Action Recognition Temporal Action Localization +1

BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions

1 code implementation29 Jan 2021 Yifan Wu, Min Gao, Min Zeng, Feiyang Chen, Min Li, Jie Zhang

Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.

Drug Discovery

KddRES: A Multi-level Knowledge-driven Dialogue Dataset for Restaurant Towards Customized Dialogue System

no code implementations17 Nov 2020 Hongru Wang, Min Li, Zimo Zhou, Gabriel Pui Cheong Fung, Kam-Fai Wong

In this paper, we publish a first Cantonese knowledge-driven Dialogue Dataset for REStaurant (KddRES) in Hong Kong, which grounds the information in multi-turn conversations to one specific restaurant.

a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs

no code implementations20 Oct 2020 Min Li, Chuanfu Xiao, Chao Yang

A mode-wise flexible Tucker decomposition algorithm is proposed to enable the switch of different solvers for the factor matrices and core tensor, and a machine-learning adaptive solver selector is applied to automatically cope with the variations of both the input data and the hardware.

DeepDyve: Dynamic Verification for Deep Neural Networks

no code implementations21 Sep 2020 Yu Li, Min Li, Bo Luo, Ye Tian, Qiang Xu

The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much.

Autonomous Driving

Robust Adaptive Beam Tracking for Mobile Millimetre Wave Communications

no code implementations3 May 2020 Chunshan Liu, Min Li, Lou Zhao, Philip Whiting, Stephen V. Hanly, Iain B. Collings, MinJian Zhao

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead.

Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors

no code implementations6 Apr 2020 Chuanfu Xiao, Chao Yang, Min Li

In this paper, we propose a new class of truncated HOSVD algorithms based on alternating least squares (ALS) for efficiently computing the low multilinear rank approximation of tensors.

Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials

no code implementations18 Jan 2020 Min Li, Zhenglong Zhou, Zhe Wu, Boxin Shi, Changyu Diao, Ping Tan

From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera.

UAV-Enabled Confidential Data Collection in Wireless Networks

no code implementations3 Jan 2020 Xiaobo Zhou, Shihao Yan, Min Li, Jun Li, Feng Shu

This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs.

On Configurable Defense against Adversarial Example Attacks

no code implementations6 Dec 2018 Bo Luo, Min Li, Yu Li, Qiang Xu

Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications.

DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding

1 code implementation CONLL 2018 Min Li, Marina Danilevsky, Sara Noeman, Yunyao Li

Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.

Spelling Correction

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing

no code implementations NeurIPS 2015 Tom Goldstein, Min Li, Xiaoming Yuan

The alternating direction method of multipliers (ADMM) is an important tool for solving complex optimization problems, but it involves minimization sub-steps that are often difficult to solve efficiently.

Identifying the Absorption Bump with Deep Learning

no code implementations17 Nov 2015 Min Li, Sudeep Gaddam, Xiaolin Li, Yinan Zhao, Jingzhe Ma, Jian Ge

In this paper, we apply Deep Learning techniques to detect the broad absorption bump.

Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems

1 code implementation2 May 2013 Tom Goldstein, Min Li, Xiaoming Yuan, Ernie Esser, Richard Baraniuk

The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps.

Numerical Analysis 65K15 G.1.6

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