California Institute of Technology

research

∙ 10/13/2019

Implicit competitive regularization in GANs

Generative adversarial networks (GANs) are capable of producing high qua...

Florian Schäfer, et al. ∙

research

∙ 01/21/2021

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle flow algorithm may b...

Joosep Pata, et al. ∙

research

∙ 07/10/2019

Out-of-Distribution Detection Using Neural Rendering Generative Models

Out-of-distribution (OoD) detection is a natural downstream task for dee...

Yujia Huang, et al. ∙

research

∙ 12/14/2021

CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning

Current state-of-the-art model-based reinforcement learning algorithms u...

Kevin Huang, et al. ∙

research

∙ 02/08/2019

FERAtt: Facial Expression Recognition with Attention Net

We present a new end-to-end network architecture for facial expression r...

Pedro D. Marrero Fernandez, et al. ∙

research

∙ 07/14/2021

A Framework for Machine Learning of Model Error in Dynamical Systems

The development of data-informed predictive models for dynamical systems...

Matthew E. Levine, et al. ∙

research

∙ 08/03/2022

MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training

We propose MinVIS, a minimal video instance segmentation (VIS) framework...

De-An Huang, et al. ∙

research

∙ 12/15/2021

Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases

Detecting social bias in text is challenging due to nuance, subjectivity...

Shrimai Prabhumoye, et al. ∙

research

∙ 10/02/2019

Learning Calibratable Policies using Programmatic Style-Consistency

We study the important and challenging problem of controllable generatio...

Eric Zhan, et al. ∙

research

∙ 07/23/2020

Learning Differentiable Programs with Admissible Neural Heuristics

We study the problem of learning differentiable functions expressed as p...

Ameesh Shah, et al. ∙

research

∙ 05/09/2020

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Learning-based control algorithms require collection of abundant supervi...

Yashwanth Kumar Nakka, et al. ∙

research

∙ 06/17/2022

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Autonomous agents have made great strides in specialist domains like Ata...

Linxi Fan, et al. ∙

research

∙ 05/02/2019

Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning

Artificial Intelligence (AI), defined in its most simple form, is a tech...

Imad Alhousseini, et al. ∙

research

∙ 10/25/2021

ZerO Initialization: Initializing Residual Networks with only Zeros and Ones

Deep neural networks are usually initialized with random weights, with a...

Jiawei Zhao, et al. ∙

research

∙ 03/03/2020

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

Trained on large datasets, deep learning (DL) can accurately classify vi...

Biagio Brattoli, et al. ∙

research

∙ 08/19/2021

Neural Operator: Learning Maps Between Function Spaces

The classical development of neural networks has primarily focused on le...

Nikola Kovachki, et al. ∙

research

∙ 12/09/2021

Extending the WILDS Benchmark for Unsupervised Adaptation

Machine learning systems deployed in the wild are often trained on a sou...

Shiori Sagawa, et al. ∙

research

∙ 06/03/2022

KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems

Learning a dynamical system requires stabilizing the unknown dynamics to...

Sahin Lale, et al. ∙

research

∙ 12/28/2019

SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning

We introduce SLM Lab, a software framework for reproducible reinforcemen...

Keng Wah Loon, et al. ∙

research

∙ 05/27/2022

Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions

A significant gap remains between today's visual pattern recognition mod...

Huaizu Jiang, et al. ∙

research

∙ 12/04/2019

Plug and Play Language Models: a Simple Approach to Controlled Text Generation

Large transformer-based language models (LMs) trained on huge text corpo...

Sumanth Dathathri, et al. ∙

research

∙ 07/12/2022

Compactly Restrictable Metric Policy Optimization Problems

We study policy optimization problems for deterministic Markov decision ...

Victor D. Dorobantu, et al. ∙

research

∙ 11/02/2018

A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

How can we efficiently gather information to optimize an unknown functio...

Jialin Song, et al. ∙

research

∙ 11/13/2018

A General Method for Amortizing Variational Filtering

We introduce the variational filtering EM algorithm, a simple, general-p...

Joseph Marino, et al. ∙

research

∙ 01/18/2020

Deep Metric Structured Learning For Facial Expression Recognition

We propose a deep metric learning model to create embedded sub-spaces wi...

Pedro D. Marrero Fernandez, et al. ∙

research

∙ 11/16/2021

Polymatrix Competitive Gradient Descent

Many economic games and machine learning approaches can be cast as compe...

Jeffrey Ma, et al. ∙

research

∙ 01/15/2022

Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations

Deep learning (DL) has proven to be an effective machine learning and co...

Daniel Lundstrom, et al. ∙

research

∙ 09/27/2019

Risk-Averse Planning Under Uncertainty

We consider the problem of designing policies for partially observable M...

Mohamadreza Ahmadi, et al. ∙

research

∙ 12/31/2019

HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a...

Bhairav Chidambaram, et al. ∙

research

∙ 05/20/2020

The Random Feature Model for Input-Output Maps between Banach Spaces

Well known to the machine learning community, the random feature model, ...

Nicholas H. Nelsen, et al. ∙

research

∙ 07/20/2021

Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix Manifold

We show that the Riemannian gradient descent algorithm on the low-rank m...

Thomas Y. Hou, et al. ∙

research

∙ 04/30/2022

Engineering flexible machine learning systems by traversing functionally invariant paths in weight space

Deep neural networks achieve human-like performance on a variety of perc...

Guruprasad Raghavan, et al. ∙

research

∙ 06/16/2022

Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings

Neuroscientists and neuroengineers have long relied on multielectrode ne...

Sabera Talukder, et al. ∙

research

∙ 01/02/2020

Using Data Imputation for Signal Separation in High Contrast Imaging

To characterize circumstellar systems in high contrast imaging, the fund...

Bin Ren, et al. ∙

research

∙ 07/08/2020

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs)...

Serim Ryou, et al. ∙

research

∙ 04/07/2021

Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

The increasing availability of data presents an opportunity to calibrate...

Andrew B. Duncan, et al. ∙

research

∙ 09/09/2021

Risk-Averse Decision Making Under Uncertainty

A large class of decision making under uncertainty problems can be descr...

Mohamadreza Ahmadi, et al. ∙

research

∙ 11/02/2021

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds

Certified robustness is a desirable property for deep neural networks in...

Yujia Huang, et al. ∙

research

∙ 06/26/2021

Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update

Representing deep neural networks (DNNs) in low-precision is a promising...

Jiawei Zhao, et al. ∙

research

∙ 01/01/2022

Turath-150K: Image Database of Arab Heritage

Large-scale image databases remain largely biased towards objects and ac...

Dani Kiyasseh, et al. ∙

research

∙ 05/12/2022

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Can a micron sized sack of interacting molecules understand, and adapt t...

William Poole, et al. ∙

research

∙ 07/20/2022

On Label Granularity and Object Localization

Weakly supervised object localization (WSOL) aims to learn representatio...

Elijah Cole, et al. ∙

research

∙ 05/23/2020

Geometric algorithms for predicting resilience and recovering damage in neural networks

Biological neural networks have evolved to maintain performance despite ...

Guruprasad Raghavan, et al. ∙

research

∙ 12/18/2018

Consistent Robust Adversarial Prediction for General Multiclass Classification

We propose a robust adversarial prediction framework for general multicl...

Rizal Fathony, et al. ∙

research

∙ 06/29/2021

ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification

African elephants are vital to their ecosystems, but their populations a...

Peter Kulits, et al. ∙

research

∙ 10/05/2021

How to Query An Oracle? Efficient Strategies to Label Data

We consider the basic problem of querying an expert oracle for labeling ...

Farshad Lahouti, et al. ∙

research

∙ 04/08/2020

The GeoLifeCLEF 2020 Dataset

Understanding the geographic distribution of species is a key concern in...

Elijah Cole, et al. ∙

research

∙ 04/03/2019

A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality

Stochastic mirror descent (SMD) is a fairly new family of algorithms tha...

Navid Azizan, et al. ∙

research

∙ 08/21/2021

Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning

Crystal-structure phase mapping is a core, long-standing challenge in ma...

Di Chen, et al. ∙

research

∙ 10/08/2021

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons

In heterogeneous rank aggregation problems, users often exhibit various ...

Yue Wu, et al. ∙

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