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05/03/2023
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Large language models (LLMs) have demonstrated remarkable abilities in r...
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01/31/2023
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Generative models have the ability to synthesize data points drawn from ...
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10/29/2022
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
This work proposes a method for using any generator network as the found...
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07/21/2022
BigIssue: A Realistic Bug Localization Benchmark
As machine learning tools progress, the inevitable question arises: How ...
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06/27/2022
ProGen2: Exploring the Boundaries of Protein Language Models
Attention-based models trained on protein sequences have demonstrated in...
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03/25/2022
A Conversational Paradigm for Program Synthesis
Program synthesis strives to generate a computer program as a solution t...
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03/15/2022
Long Document Summarization with Top-down and Bottom-up Inference
Text summarization aims to condense long documents and retain key inform...
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06/15/2020
Learning Latent Space Energy-Based Prior Model
The generator model assumes that the observed example is generated by a ...
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06/12/2020
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC
Learning energy-based model (EBM) requires MCMC sampling of the learned ...
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06/10/2020
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
This paper proposes a joint training method to learn both the variationa...
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12/04/2019
Learning Deep Generative Models with Short Run Inference Dynamics
This paper studies the fundamental problem of learning deep generative m...
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12/02/2019
Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based...
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11/26/2019
Representation Learning: A Statistical Perspective
Learning representations of data is an important problem in statistics a...
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05/07/2019
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks
Recent work on mode connectivity in the loss landscape of deep neural ne...
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04/22/2019
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model
This paper studies a curious phenomenon in learning energy-based model (...
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03/29/2019
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models
This study investigates the effects Markov Chain Monte Carlo (MCMC) samp...
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12/28/2018
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
This paper proposes the divergence triangle as a framework for joint tra...
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03/02/2018