Search Results for author: Lu Mi

Found 15 papers, 5 papers with code

Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations

1 code implementation25 Jan 2024 Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li

Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations.

Attention for Causal Relationship Discovery from Biological Neural Dynamics

1 code implementation12 Nov 2023 Ziyu Lu, Anika Tabassum, Shruti Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim

This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks.

Representation Learning

Learning Time-Invariant Representations for Individual Neurons from Population Dynamics

1 code implementation NeurIPS 2023 Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül

This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit.

Self-Supervised Learning

LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud

no code implementations29 Sep 2023 Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, FatemehSadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov

In the current user-server interaction paradigm of prompted generation with large language models (LLM) on cloud, the server fully controls the generation process, which leaves zero options for users who want to keep the generated text to themselves.

im2nerf: Image to Neural Radiance Field in the Wild

no code implementations8 Sep 2022 Lu Mi, Abhijit Kundu, David Ross, Frank Dellaert, Noah Snavely, Alireza Fathi

We take a step towards addressing this shortcoming by introducing a model that encodes the input image into a disentangled object representation that contains a code for object shape, a code for object appearance, and an estimated camera pose from which the object image is captured.

Novel View Synthesis Object

Subgraph Frequency Distribution Estimation using Graph Neural Networks

no code implementations14 Jul 2022 Zhongren Chen, Xinyue Xu, Shengyi Jiang, Hao Wang, Lu Mi

Small subgraphs (graphlets) are important features to describe fundamental units of a large network.

Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework

1 code implementation13 Oct 2021 Lu Mi, Tianxing He, Core Francisco Park, Hao Wang, Yue Wang, Nir Shavit

In this work, we show how data labeled with semantically continuous attributes can be utilized to conduct a quantitative evaluation of latent-space interpolation algorithms, for variational autoencoders.

Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity

no code implementations ICLR 2022 Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravinthan Samuel, Srinivas C Turaga

Brain-wide measurements of activity and anatomical connectivity of the $\textit{C. elegans}$ nervous system in principle allow for the development of detailed mechanistic computational models.

Learning Guided Electron Microscopy with Active Acquisition

1 code implementation7 Jan 2021 Lu Mi, Hao Wang, Yaron Meirovitch, Richard Schalek, Srinivas C. Turaga, Jeff W. Lichtman, Aravinthan D. T. Samuel, Nir Shavit

Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.

Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate

no code implementations28 Sep 2019 Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.

regression

A Probe Towards Understanding GAN and VAE Models

no code implementations13 Dec 2018 Lu Mi, Macheng Shen, Jingzhao Zhang

This project report compares some known GAN and VAE models proposed prior to 2017.

Generative Adversarial Network

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics

no code implementations CVPR 2019 Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit

Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching.

Clustering General Classification +4

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