Search Results for author: Zhenliang Zhang

Found 7 papers, 3 papers with code

On the Emergence of Symmetrical Reality

no code implementations26 Jan 2024 Zhenliang Zhang, Zeyu Zhang, Ziyuan Jiao, Yao Su, Hangxin Liu, Wei Wang, Song-Chun Zhu

Artificial intelligence (AI) has revolutionized human cognitive abilities and facilitated the development of new AI entities capable of interacting with humans in both physical and virtual environments.

Mixed Reality

Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

1 code implementation16 Oct 2023 Rujie Wu, Xiaojian Ma, Zhenliang Zhang, Wei Wang, Qing Li, Song-Chun Zhu, Yizhou Wang

We even conceived a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems.

Few-Shot Learning Logical Reasoning +1

A Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps

no code implementations14 Jan 2023 Hangxin Liu, Zeyu Zhang, Ziyuan Jiao, Zhenliang Zhang, Minchen Li, Chenfanfu Jiang, Yixin Zhu, Song-Chun Zhu

In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks.

A One-bit, Comparison-Based Gradient Estimator

1 code implementation6 Oct 2020 HanQin Cai, Daniel Mckenzie, Wotao Yin, Zhenliang Zhang

By treating the gradient as an unknown signal to be recovered, we show how one can use tools from one-bit compressed sensing to construct a robust and reliable estimator of the normalized gradient.

Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling

1 code implementation29 Mar 2020 HanQin Cai, Daniel Mckenzie, Wotao Yin, Zhenliang Zhang

We consider the problem of minimizing a high-dimensional objective function, which may include a regularization term, using (possibly noisy) evaluations of the function.

Hypothesis Testing in Feedforward Networks with Broadcast Failures

no code implementations19 Nov 2012 Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran

In the case where the flipping probabilities converge to 1/2, we derive a necessary condition on the convergence rate of the flipping probabilities such that the decisions still converge to the underlying truth.

Two-sample testing

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