Search Results for author: Min Lin

Found 51 papers, 37 papers with code

Sailor: Open Language Models for South-East Asia

1 code implementation4 Apr 2024 Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Wei Lu, Min Lin

We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.

Language Modelling Question Answering +1

Beyond Memorization: The Challenge of Random Memory Access in Language Models

1 code implementation12 Mar 2024 Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin

Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.

Memorization Open-Domain Question Answering

Graph Diffusion Policy Optimization

1 code implementation26 Feb 2024 Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li, Wei Chen, Min Lin

Recent research has made significant progress in optimizing diffusion models for specific downstream objectives, which is an important pursuit in fields such as graph generation for drug design.

Graph Generation

Purifying Large Language Models by Ensembling a Small Language Model

no code implementations19 Feb 2024 Tianlin Li, Qian Liu, Tianyu Pang, Chao Du, Qing Guo, Yang Liu, Min Lin

The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources.

Data Poisoning Language Modelling

Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

1 code implementation13 Feb 2024 Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, Min Lin

A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use.

Language Modelling Large Language Model

Test-Time Backdoor Attacks on Multimodal Large Language Models

1 code implementation13 Feb 2024 Dong Lu, Tianyu Pang, Chao Du, Qian Liu, Xianjun Yang, Min Lin

Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase.

Backdoor Attack

Locality Sensitive Sparse Encoding for Learning World Models Online

no code implementations23 Jan 2024 Zichen Liu, Chao Du, Wee Sun Lee, Min Lin

Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents.

Continual Learning Model-based Reinforcement Learning

Benchmarking Large Multimodal Models against Common Corruptions

1 code implementation22 Jan 2024 Jiawei Zhang, Tianyu Pang, Chao Du, Yi Ren, Bo Li, Min Lin

This technical report aims to fill a deficiency in the assessment of large multimodal models (LMMs) by specifically examining the self-consistency of their outputs when subjected to common corruptions.

Benchmarking

Zero Bubble Pipeline Parallelism

1 code implementation30 Nov 2023 Penghui Qi, Xinyi Wan, Guangxing Huang, Min Lin

Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable.

Scheduling

Automatic Functional Differentiation in JAX

1 code implementation30 Nov 2023 Min Lin

We present a set of primitive operators that serve as foundational building blocks for constructing several key types of functionals.

Instant3D: Instant Text-to-3D Generation

no code implementations14 Nov 2023 Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, Xiangyu Xu

Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network.

Negation Text to 3D

Finetuning Text-to-Image Diffusion Models for Fairness

1 code implementation11 Nov 2023 Xudong Shen, Chao Du, Tianyu Pang, Min Lin, Yongkang Wong, Mohan Kankanhalli

The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases.

Fairness

On Memorization in Diffusion Models

2 code implementations4 Oct 2023 Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang

Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum.

Denoising Memorization

Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform

1 code implementation29 Sep 2023 Shengyi Huang, Jiayi Weng, Rujikorn Charakorn, Min Lin, Zhongwen Xu, Santiago Ontañón

Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time.

reinforcement-learning

LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

1 code implementation25 Jul 2023 Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, Min Lin

This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks.

In-Context Learning

On Evaluating Adversarial Robustness of Large Vision-Language Models

1 code implementation NeurIPS 2023 Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin

Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.

Adversarial Robustness multimodal generation +1

Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

2 code implementations3 May 2023 Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities.

From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning

1 code implementation17 Apr 2023 Qian Liu, Fan Zhou, Zhengbao Jiang, Longxu Dou, Min Lin

Empirical results on various benchmarks validate that the integration of SQL execution leads to significant improvements in zero-shot scenarios, particularly in table reasoning.

Zero-shot Generalization

Exploring Incompatible Knowledge Transfer in Few-shot Image Generation

1 code implementation CVPR 2023 Yunqing Zhao, Chao Du, Milad Abdollahzadeh, Tianyu Pang, Min Lin, Shuicheng Yan, Ngai-Man Cheung

To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.

Image Generation Transfer Learning

A Recipe for Watermarking Diffusion Models

1 code implementation17 Mar 2023 Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Ngai-Man Cheung, Min Lin

Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.

Better Diffusion Models Further Improve Adversarial Training

2 code implementations9 Feb 2023 Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan

Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70. 69\%$ and $42. 67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i. e. improving upon previous state-of-the-art models by $+4. 58\%$ and $+8. 03\%$.

Denoising

Does Federated Learning Really Need Backpropagation?

1 code implementation28 Jan 2023 Haozhe Feng, Tianyu Pang, Chao Du, Wei Chen, Shuicheng Yan, Min Lin

BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server.

Federated Learning Quantization

IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation

no code implementations ICCV 2023 Yun Wang, Cheng Chi, Min Lin, Xin Yang

This approach circulates high-resolution estimated information (scene flow and feature) from the preceding iteration back to the low-resolution layer of the current iteration.

Autonomous Driving Computational Efficiency +1

Mutual Information Regularized Offline Reinforcement Learning

1 code implementation NeurIPS 2023 Xiao Ma, Bingyi Kang, Zhongwen Xu, Min Lin, Shuicheng Yan

In this work, we propose a novel MISA framework to approach offline RL from the perspective of Mutual Information between States and Actions in the dataset by directly constraining the policy improvement direction.

D4RL Offline RL +2

Optical Neural Ordinary Differential Equations

no code implementations26 Sep 2022 Yun Zhao, Hang Chen, Min Lin, Haiou Zhang, Tao Yan, Xing Lin, Ruqi Huang, Qionghai Dai

Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.

Image Classification Trajectory Prediction

$O(N^2)$ Universal Antisymmetry in Fermionic Neural Networks

no code implementations26 May 2022 Tianyu Pang, Shuicheng Yan, Min Lin

In this paper, we substitute the Slater determinant with a pairwise antisymmetry construction, which is easy to implement and can reduce the computational cost to $O(N^2)$.

Variational Monte Carlo

Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

1 code implementation21 Feb 2022 Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan

The trade-off between robustness and accuracy has been widely studied in the adversarial literature.

Inductive Bias

Causal Attention for Interpretable and Generalizable Graph Classification

1 code implementation30 Dec 2021 Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua

To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.

Graph Attention Graph Classification

LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction

1 code implementation27 Oct 2021 Kun Li, Meng Li, Yanling Li, Min Lin

The traditional trend prediction models can better predict the short trend than the long trend.

Outage Constrained Robust Secure Beamforming in Cognitive Satellite-Aerial Networks

no code implementations13 May 2021 Bai Zhao, Min Lin, Ming Cheng, Wei-Ping Zhu, Naofal Al-Dhahir

This paper proposes a robust beamforming scheme to enhance the physical layer security (PLS) of multicast transmission in a cognitive satellite and aerial network (CSAN) operating in the millimeter wave frequency band.

Continual Learning from the Perspective of Compression

no code implementations ICML Workshop LifelongML 2020 Xu He, Min Lin

We compare these approaches in terms of both compression and forgetting and empirically study the reasons that limit the performance of continual learning methods based on variational posterior approximation.

Continual Learning

Online Continual Learning with Maximal Interfered Retrieval

2 code implementations NeurIPS 2019 Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia

Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks.

Class Incremental Learning Retrieval

Online Continual Learning with Maximally Interfered Retrieval

1 code implementation11 Aug 2019 Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, Tinne Tuytelaars

Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks.

Continual Learning Retrieval

Conditional Computation for Continual Learning

no code implementations16 Jun 2019 Min Lin, Jie Fu, Yoshua Bengio

In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example.

Continual Learning

Gradient based sample selection for online continual learning

4 code implementations NeurIPS 2019 Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio

To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal.

Class Incremental Learning

On the Spectral Bias of Neural Networks

2 code implementations ICLR 2019 Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy.

Softmax GAN

4 code implementations20 Apr 2017 Min Lin

In the generator training phase, the target is to assign equal probability to all data points in the batch, each with probability $\frac{1}{M+N}$.

Generative Adversarial Network

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

2 code implementations3 Dec 2015 Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang

This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.

BIG-bench Machine Learning Clustering +2

Correntropy Induced L2 Graph for Robust Subspace Clustering

no code implementations18 Jan 2015 Canyi Lu, Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin

In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces.

Clustering graph construction

Purine: A bi-graph based deep learning framework

1 code implementation19 Dec 2014 Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan

In this paper, we introduce a novel deep learning framework, termed Purine.

Network In Network

17 code implementations16 Dec 2013 Min Lin, Qiang Chen, Shuicheng Yan

With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.

General Classification Image Classification

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