Search Results for author: Erik Nijkamp

Found 21 papers, 8 papers with code

ProGen2: Exploring the Boundaries of Protein Language Models

1 code implementation27 Jun 2022 Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani

Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.

A Conversational Paradigm for Program Synthesis

1 code implementation25 Mar 2022 Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong

We train a family of large language models, called CodeGen, on natural language and programming language data.

Benchmark Language Modelling +1

Long Document Summarization with Top-down and Bottom-up Inference

no code implementations15 Mar 2022 Bo Pang, Erik Nijkamp, Wojciech Kryściński, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents.

Long Document Summarization with Top-Down and Bottom-Up Representation Inference

no code implementations29 Sep 2021 Bo Pang, Erik Nijkamp, Wojciech Maciej Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents.

Document Summarization

MCMC Should Mix: Learning Energy-Based Model with Flow-Based Backbone

no code implementations ICLR 2022 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space.

Generative Text Modeling through Short Run Inference

1 code implementation EACL 2021 Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu

It is initialized from the prior distribution of the latent variable and then runs a small number (e. g., 20) of Langevin dynamics steps guided by its posterior distribution.

Language Modelling

Learning Latent Space Energy-Based Prior Model

1 code implementation NeurIPS 2020 Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

Due to the low dimensionality of the latent space and the expressiveness of the top-down network, a simple EBM in latent space can capture regularities in the data effectively, and MCMC sampling in latent space is efficient and mixes well.

Anomaly Detection Text Generation

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC

no code implementations12 Jun 2020 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm.

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

no code implementations CVPR 2020 Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu

This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).

Anomaly Detection

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

no code implementations ECCV 2020 Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.

A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks

no code implementations7 May 2019 Lior Deutsch, Erik Nijkamp, Yu Yang

Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths.

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

2 code implementations29 Mar 2019 Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu

On the other hand, ConvNet potentials learned with non-convergent MCMC do not have a valid steady-state and cannot be considered approximate unnormalized densities of the training data because long-run MCMC samples differ greatly from observed images.

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model

1 code implementation28 Dec 2018 Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model.

Building a Telescope to Look Into High-Dimensional Image Spaces

no code implementations2 Mar 2018 Mitch Hill, Erik Nijkamp, Song-Chun Zhu

However, characterizing a learned probability density to uncover the Hopfield memories of the model, encoded by the structure of the local modes, remains an open challenge.

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