46 citations · 177 across the 41 of their papers we have counts for
44 papers
Visualizing the World Model in an Agent's Value Function
Shawheen Naderi, Fatemeh Akherati, Yubei Chen
Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions which hold everything about…
Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction
Xinyi Li, Zaishuo Xia, Chenjie Hao +1
World models are expected to support imagination over extended temporal horizons, yet most are still trained through local few-step prediction objectives and deployed by recursivel…
Toward a mechanistic understanding of inference in visual cortex and diffusion models
Zeyu Yun, Alexander Belsten, Dasheng Bi +3
We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters. The…
Improving Weak World Models Behind Strong Agents in Atari Pong
Yukuan Lu, Zaishuo Xia, Weyl Lu +1
Strong world-model agents frequently contain weak world models. We study this agent-world-model gap by reproducing five visual world-model agents in Atari Pong: DreamerV3, DIAMOND,…
MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
Gao Zhu, Zaishuo Xia, Yubei Chen
We ask whether the representational hierarchy seen in perception, from local primitives such as edges to higher level structures such as parts and objects, can be established for m…
Deep Networks Favor Simple Data
Weyl Lu, Chenjie Hao, Yubei Chen
Estimated density is often interpreted as indicating how typical a sample is under a model. Yet deep models trained on one dataset can assign higher density to simpler out-of-distr…
Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learning
Nhat Tran, Chenjie Hao, Alexander Stameroff +2
Accurate, high-performance radio-frequency (RF) filter circuits are ubiquitous in radio-frequency communication and sensing systems for accepting and rejecting signals at desired f…
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
Chenjie Hao, Weyl Lu, Yuko Ishiwaka +3
When a model knows when it does not know, many possibilities emerge. The first question is how to enable a model to recognize that it does not know. A promising approach is to use…
SmallWorlds: Assessing Dynamics Understanding of World Models in Isolated Environments
Xinyi Li, Zaishuo Xia, Weyl Lu +2
Current world models lack a unified and controlled setting for systematic evaluation, making it difficult to assess whether they truly capture the underlying rules that govern envi…
Scaling Non-Parametric Sampling with Representation
Vincent Lu, Aaron Truong, Zeyu Yun +1
Scaling and architectural advances have produced strikingly photorealistic image generative models, yet their mechanisms still remain opaque. Rather than advancing scaling, our goa…
Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior
Renzhi He, Haowen Zhou, Yubei Chen +1
Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical to…
Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models
Zaishuo Xia, Yukuan Lu, Xinyi Li +2
A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical state of both the embodied agent and i…
Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs
Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong +7
Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental…
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning
Chenjie Hao, Weyl Lu, Yifan Xu +1
An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acqui…
Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications
Yu-Chieh Chao, Yubei Chen, Weiwei Wang +4
Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry…
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis
Chun-Hsiao Yeh, Jiayun Wang, Andrew D. Graham +5
Accurate diagnosis of ocular surface diseases is critical in optometry and ophthalmology, which hinge on integrating clinical data sources (e.g., meibography imaging and clinical m…
PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro with In-Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout
Zhiyu Chen, Ziyuan Wen, Weier Wan +6
Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-eff…
Pose-Aware Self-Supervised Learning with Viewpoint Trajectory Regularization
Jiayun Wang, Yubei Chen, Stella X. Yu
Learning visual features from unlabeled images has proven successful for semantic categorization, often by mapping different of the same object to the same feature to achie…
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
Chun-Hsiao Yeh, Ta-Ying Cheng, He-Yen Hsieh +6
Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examp…
Strong Interference HVSR Data Processing and Denoising: HVSR Curve Reconstruction Method based on UPEMD
Bingxuan Song, Fuxing Han, Yubei Chen +3
Urban areas pose a challenge for the application of the H/V method due to a high degree of artificial noise. The existing methods fall short in reducing the noise of strong interfe…
URLOST: Unsupervised Representation Learning without Stationarity or Topology
Zeyu Yun, Juexiao Zhang, Yann LeCun +1
Unsupervised representation learning has seen tremendous progress. However, it is constrained by its reliance on domain specific stationarity and topology, a limitation not found i…
Unsupervised Feature Learning with Emergent Data-Driven Prototypicality
Yunhui Guo, Youren Zhang, Yubei Chen +1
Given an image set without any labels, our goal is to train a model that maps each image to a point in a feature space such that, not only proximity indicates visual similarity, bu…
Variance-Covariance Regularization Improves Representation Learning
Jiachen Zhu, Katrina Evtimova, Yubei Chen +2
Transfer learning plays a key role in advancing machine learning models, yet conventional supervised pretraining often undermines feature transferability by prioritizing features t…
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Shengbang Tong, Yubei Chen, Yi Ma +1
Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are…