Search Results for author: Yiwei Lyu

Found 16 papers, 10 papers with code

Step-Calibrated Diffusion for Biomedical Optical Image Restoration

2 code implementations20 Mar 2024 Yiwei Lyu, Sung Jik Cha, Cheng Jiang, Asadur Chowdury, Xinhai Hou, Edward Harake, Akhil Kondepudi, Christian Freudiger, Honglak Lee, Todd C. Hollon

Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task.

Image Generation Image Restoration

A self-supervised framework for learning whole slide representations

no code implementations9 Feb 2024 Xinhai Hou, Cheng Jiang, Akhil Kondepudi, Yiwei Lyu, Asadur Zaman Chowdury, Honglak Lee, Todd C. Hollon

Self-supervised representation learning can achieve high-quality WSI visual feature learning for downstream diagnostic tasks, such as cancer diagnosis or molecular genetic prediction.

Language Modelling Representation Learning +1

TOD-Flow: Modeling the Structure of Task-Oriented Dialogues

1 code implementation7 Dec 2023 Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee

Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.

Dialog Act Classification Response Generation

Code Models are Zero-shot Precondition Reasoners

no code implementations16 Nov 2023 Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Zhe Liu, Dong-Ki Kim, Dongsub Shim, Moontae Lee, Honglak Lee

One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.

Decision Making

Fine-grained Text Style Transfer with Diffusion-Based Language Models

1 code implementation31 May 2023 Yiwei Lyu, Tiange Luo, Jiacheng Shi, Todd C. Hollon, Honglak Lee

Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain.

Style Transfer Text Style Transfer

Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation

no code implementations22 May 2023 Yiwei Lyu, Wenhao Luo, John M. Dolan

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems.

Autonomous Driving Computational Efficiency

Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning

no code implementations13 Apr 2023 Wenli Xiao, Yiwei Lyu, John Dolan

This design enables efficient synthesis of shields to monitor agents in complex environments without coordination overheads.

Multi-agent Reinforcement Learning reinforcement-learning +1

Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control

1 code implementation10 Nov 2022 Xiang Fan, Yiwei Lyu, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency

Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions.

Attribute Fairness +2

DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations

1 code implementation3 Mar 2022 Yiwei Lyu, Paul Pu Liang, Zihao Deng, Ruslan Salakhutdinov, Louis-Philippe Morency

The ability for a human to understand an Artificial Intelligence (AI) model's decision-making process is critical in enabling stakeholders to visualize model behavior, perform model debugging, promote trust in AI models, and assist in collaborative human-AI decision-making.

Decision Making Disentanglement +2

High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning

1 code implementation2 Mar 2022 Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov

Many real-world problems are inherently multimodal, from spoken language, gestures, and paralinguistics humans use to communicate, to force, proprioception, and visual sensors on robots.

Representation Learning Time Series Analysis +2

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

2 code implementations15 Jul 2021 Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, Louis-Philippe Morency

In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.

Representation Learning

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

2 code implementations NAACL 2021 Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency

Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e. g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence.

Benchmarking Sentence +2

The State and Future of Genetic Improvement

no code implementations27 Jun 2019 William B. Langdon, Westley Weimer, Christopher Timperley, Oliver Krauss, Zhen Yu Ding, Yiwei Lyu, Nicolas Chausseau, Eric Schulte, Shin Hwei Tan, Kevin Leach, Yu Huang, Gabin An

We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2019.

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