Multimodal Language Analysis with Recurrent Multistage Fusion. - Paul Pu Liang; Ziyin Liu 001; Amir Zadeh 001; Louis-Philippe Morency (EMNLP, 2018)

BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning. - Yixiu Zhao; Ziyin Liu 001 (CoRR, 2018)

Deep Gamblers: Learning to Abstain with Portfolio Theory. - Liu Ziyin 001; Zhikang Wang; Paul Pu Liang; Ruslan Salakhutdinov; Louis-Philippe Morency; Masahito Ueda (NeurIPS, 2019)

Neural Networks Fail to Learn Periodic Functions and How to Fix It. - Liu Ziyin 001; Tilman Hartwig; Masahito Ueda (NeurIPS, 2020)

Think Locally, Act Globally: Federated Learning with Local and Global Representations. - Paul Pu Liang; Terrance Liu; Liu Ziyin 001; Ruslan Salakhutdinov; Louis-Philippe Morency (CoRR, 2020)

LaProp: a Better Way to Combine Momentum with Adaptive Gradient. - Liu Ziyin 001; Zhikang Wang; Masahito Ueda (CoRR, 2020)

Learning Not to Learn in the Presence of Noisy Labels. - Liu Ziyin 001; Blair Chen; Ru Wang; Paul Pu Liang; Ruslan Salakhutdinov; Louis-Philippe Morency; Masahito Ueda (CoRR, 2020)

Volumization as a Natural Generalization of Weight Decay. - Liu Ziyin 001; Zihao Wang 001; Makoto Yamada; Masahito Ueda (CoRR, 2020)

An Investigation of how Label Smoothing Affects Generalization. - Blair Chen; Liu Ziyin 001; Zihao Wang 001; Paul Pu Liang (CoRR, 2020)

Stochastic Gradient Descent with Large Learning Rate. - Kangqiao Liu; Liu Ziyin 001; Masahito Ueda (CoRR, 2020)

Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent. - Kangqiao Liu; Liu Ziyin 001; Masahito Ueda (ICML, 2021)

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment. - Paul Pu Liang; Peter Wu; Liu Ziyin 001; Louis-Philippe Morency; Ruslan Salakhutdinov (ACM Multimedia, 2021)

On the distributional properties of adaptive gradients. - Zhiyi Zhang; Ziyin Liu 001 (UAI, 2021)

On Minibatch Noise: Discrete-Time SGD, Overparametrization, and Bayes. - Liu Ziyin 001; Kangqiao Liu; Takashi Mori; Masahito Ueda (CoRR, 2021)

Logarithmic landscape and power-law escape rate of SGD. - Takashi Mori; Liu Ziyin 001; Kangqiao Liu; Masahito Ueda (CoRR, 2021)

What Data Augmentation Do We Need for Deep-Learning-Based Finance? - Liu Ziyin 001; Kentaro Minami; Kentaro Imajo (CoRR, 2021)

SGD May Never Escape Saddle Points. - Liu Ziyin 001; Botao Li; Masahito Ueda (CoRR, 2021)

Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction. - Liu Ziyin 001; Kentaro Minami; Kentaro Imajo (ICAIF, 2022)

Strength of Minibatch Noise in SGD. - Liu Ziyin 001; Kangqiao Liu; Takashi Mori; Masahito Ueda (ICLR, 2022)

SGD Can Converge to Local Maxima. - Liu Ziyin 001; Botao Li; James B. Simon; Masahito Ueda (ICLR, 2022)

Power-Law Escape Rate of SGD. - Takashi Mori; Liu Ziyin 001; Kangqiao Liu; Masahito Ueda (ICML, 2022)

Posterior Collapse of a Linear Latent Variable Model. - Zihao Wang 001; Liu Ziyin 001 (NeurIPS, 2022)

Exact Solutions of a Deep Linear Network. - Liu Ziyin 001; Botao Li; Xiangming Meng (NeurIPS, 2022)

Stochastic Neural Networks with Infinite Width are Deterministic. - Liu Ziyin 001; Hanlin Zhang 002; Xiangming Meng; Yuting Lu; Eric P. Xing; Masahito Ueda (CoRR, 2022)

Exact Phase Transitions in Deep Learning. - Liu Ziyin 001; Masahito Ueda (CoRR, 2022)

Sparsity by Redundancy: Solving L1 with a Simple Reparametrization. - Liu Ziyin 001; Zihao Wang 001 (CoRR, 2022)

What shapes the loss landscape of self supervised learning? - Liu Ziyin 001; Ekdeep Singh Lubana; Masahito Ueda; Hidenori Tanaka (ICLR, 2023)

spred: Solving L1 Penalty with SGD. - Liu Ziyin 001; Zihao Wang 001 (ICML, 2023)

On the Stepwise Nature of Self-Supervised Learning. - James B. Simon; Maksis Knutins; Liu Ziyin 001; Daniel Geisz; Abraham J. Fetterman; Joshua Albrecht (ICML, 2023)

The Probabilistic Stability of Stochastic Gradient Descent. - Liu Ziyin 001; Botao Li; Tomer Galanti; Masahito Ueda (CoRR, 2023)

Law of Balance and Stationary Distribution of Stochastic Gradient Descent. - Liu Ziyin 001; Hongchao Li; Masahito Ueda (CoRR, 2023)

Symmetry Leads to Structured Constraint of Learning. - Liu Ziyin 001 (CoRR, 2023)

Symmetry Induces Structure and Constraint of Learning. - Liu Ziyin 001 (ICML, 2024)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent. - Liu Ziyin 001; Mingze Wang; Hongchao Li; Lei Wu (NeurIPS, 2024)

When Does Feature Learning Happen? Perspective from an Analytically Solvable Model. - Yizhou Xu; Liu Ziyin 001 (CoRR, 2024)

The Implicit Bias of Gradient Noise: A Symmetry Perspective. - Liu Ziyin 001; Mingze Wang; Lei Wu (CoRR, 2024)

Remove Symmetries to Control Model Expressivity. - Liu Ziyin 001; Yizhou Xu; Isaac L. Chuang (CoRR, 2024)

Self-Assembly of a Biologically Plausible Learning Circuit. - Qianli Liao; Liu Ziyin 001; Yulu Gan; Brian Cheung; Mark Harnett; Tomaso A. Poggio (CoRR, 2024)

Formation of Representations in Neural Networks. - Liu Ziyin 001; Isaac L. Chuang; Tomer Galanti; Tomaso A. Poggio (ICLR, 2025)

Remove Symmetries to Control Model Expressivity and Improve Optimization. - Liu Ziyin 001; Yizhou Xu; Isaac L. Chuang (ICLR, 2025)

Three Mechanisms of Feature Learning in a Linear Network. - Yizhou Xu; Ziyin Liu 001 (ICLR, 2025)

Compositional Generalization Requires More Than Disentangled Representations. - Qiyao Liang; Daoyuan Qian; Liu Ziyin 001; Ila Fiete (CoRR, 2025)

Parameter Symmetry Breaking and Restoration Determines the Hierarchical Learning in AI Systems. - Liu Ziyin 001; Yizhou Xu; Tomaso A. Poggio; Isaac L. Chuang (CoRR, 2025)

Understanding the Emergence of Multimodal Representation Alignment. - Megan Tjandrasuwita; Chanakya Ekbote; Liu Ziyin 001; Paul Pu Liang (CoRR, 2025)

Heterosynaptic Circuits Are Universal Gradient Machines. - Liu Ziyin 001; Isaac L. Chuang; Tomaso A. Poggio (CoRR, 2025)

Neural Thermodynamics I: Entropic Forces in Deep and Universal Representation Learning. - Liu Ziyin 001; Yizhou Xu; Isaac L. Chuang (CoRR, 2025)

Emergence of Hebbian Dynamics in Regularized Non-Local Learners. - David Koplow; Tomaso A. Poggio; Liu Ziyin 001 (CoRR, 2025)

Proof of a perfect platonic representation hypothesis. - Ziyin Liu 001; Isaac L. Chuang (CoRR, 2025)