Search Results for author: Weiyang Liu

Found 45 papers, 24 papers with code

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

1 code implementation21 Sep 2023 Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu

Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.

Ranked #15 on Math Word Problem Solving on MATH (using extra training data)

Arithmetic Reasoning GSM8K +4

Learning Disentangled Avatars with Hybrid 3D Representations

no code implementations12 Sep 2023 Yao Feng, Weiyang Liu, Timo Bolkart, Jinlong Yang, Marc Pollefeys, Michael J. Black

Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.


Controlling Text-to-Image Diffusion by Orthogonal Finetuning

no code implementations12 Jun 2023 Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf

To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks.

Nonparametric Iterative Machine Teaching

1 code implementation5 Jun 2023 Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, James Kwok

In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the teacher provides examples to the learner iteratively such that the learner can achieve fast convergence to a target model.

MeshDiffusion: Score-based Generative 3D Mesh Modeling

1 code implementation14 Mar 2023 Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu

We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation.

Scene Generation

Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap

no code implementations11 Mar 2023 Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf

We then use hyperspherical uniformity (which characterizes the degree of uniformity on the unit hypersphere) as a unified framework to quantify these two objectives.

Human-in-the-Loop Mixup

1 code implementation2 Nov 2022 Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller

We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.

Iterative Teaching by Data Hallucination

1 code implementation31 Oct 2022 Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i. e., a pool of finite samples), which greatly limits the teacher's capability.

Continual Learning by Modeling Intra-Class Variation

1 code implementation11 Oct 2022 Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu

It has been observed that neural networks perform poorly when the data or tasks are presented sequentially.

Continual Learning

Structural Causal 3D Reconstruction

no code implementations20 Jul 2022 Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller, Bernhard Schölkopf

This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images.

3D Object Reconstruction 3D Reconstruction +2

Data-Efficient Learning via Minimizing Hyperspherical Energy

no code implementations30 Jun 2022 Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang

Finally, we demonstrate the empirical performance of MHEAL in a wide range of applications on data-efficient learning, including deep clustering, distribution matching, version space sampling and deep active learning.

Active Learning Deep Clustering

Locality Sensitive Teaching

no code implementations NeurIPS 2021 Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava

However, as one of the most influential and practical MT paradigms, iterative machine teaching (IMT) is prohibited on IoT devices due to its inefficient and unscalable algorithms.

Towards Principled Disentanglement for Domain Generalization

1 code implementation CVPR 2022 HANLIN ZHANG, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG).

Disentanglement Domain Generalization

Provable Lifelong Learning of Representations

no code implementations27 Oct 2021 Xinyuan Cao, Weiyang Liu, Santosh S. Vempala

We prove that for any desired accuracy on all tasks, the dimension of the representation remains close to that of the underlying representation.

Continual Learning

Iterative Teaching by Label Synthesis

no code implementations NeurIPS 2021 Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

Self-Supervised 3D Face Reconstruction via Conditional Estimation

no code implementations ICCV 2021 Yandong Wen, Weiyang Liu, Bhiksha Raj, Rita Singh

We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos.

3D Face Reconstruction Disentanglement

Pre-training Molecular Graph Representation with 3D Geometry

1 code implementation ICLR 2022 Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang

However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation.

Graph Representation Learning Self-Supervised Learning

SphereFace Revived: Unifying Hyperspherical Face Recognition

1 code implementation12 Sep 2021 Weiyang Liu, Yandong Wen, Bhiksha Raj, Rita Singh, Adrian Weller

As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin.

Face Recognition

SphereFace2: Binary Classification is All You Need for Deep Face Recognition

no code implementations ICLR 2022 Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh

In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization.

Binary Classification Classification +2

Learning with Hyperspherical Uniformity

1 code implementation2 Mar 2021 Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller

Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.

Inductive Bias L2 Regularization

Iterative Graph Self-Distillation

no code implementations23 Oct 2020 HANLIN ZHANG, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs.

Contrastive Learning Graph Learning +1

Neural Similarity Learning

1 code implementation NeurIPS 2019 Weiyang Liu, Zhen Liu, James M. Rehg, Le Song

By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs.

Few-Shot Learning

Regularizing Neural Networks via Minimizing Hyperspherical Energy

1 code implementation CVPR 2020 Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song

Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.

Meta Architecture Search

1 code implementation NeurIPS 2019 Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.

Bayesian Inference Few-Shot Learning +1

Coupled Variational Bayes via Optimization Embedding

1 code implementation NeurIPS 2018 Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.

Variational Inference

Simultaneous Edge Alignment and Learning

3 code implementations ECCV 2018 Zhiding Yu, Weiyang Liu, Yang Zou, Chen Feng, Srikumar Ramalingam, B. V. K. Vijaya Kumar, Jan Kautz

Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.

Edge Detection Representation Learning

Disjoint Mapping Network for Cross-modal Matching of Voices and Faces

no code implementations ICLR 2019 Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces.

Learning towards Minimum Hyperspherical Energy

4 code implementations NeurIPS 2018 Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.

Decoupled Networks

1 code implementation CVPR 2018 Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.

Iterative Learning with Open-set Noisy Labels

1 code implementation CVPR 2018 Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia

We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.

Additive Margin Softmax for Face Verification

9 code implementations17 Jan 2018 Feng Wang, Weiyang Liu, Haijun Liu, Jian Cheng

In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.

Face Verification Metric Learning

Deep Hyperspherical Learning

no code implementations NeurIPS 2017 Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song

In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.

Representation Learning

Towards Black-box Iterative Machine Teaching

no code implementations ICML 2018 Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song

We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.

Iterative Machine Teaching

2 code implementations ICML 2017 Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song

Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.

SphereFace: Deep Hypersphere Embedding for Face Recognition

13 code implementations CVPR 2017 Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

Face Identification Face Recognition +1

Large-Margin Softmax Loss for Convolutional Neural Networks

2 code implementations7 Dec 2016 Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs).

General Classification

Robust Elastic Net Regression

no code implementations15 Nov 2015 Weiyang Liu, Rongmei Lin, Meng Yang

We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound).


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