b'Borealis AI'

research

∙ 11/15/2020

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

Deep Reinforcement Learning (DRL) has recently achieved significant adva...

Zihan Ding, et al. ∙

research

∙ 08/07/2019

Continuous Graph Flow for Flexible Density Estimation

In this paper, we propose Continuous Graph Flow, a generative continuous...

Zhiwei Deng, et al. ∙

research

∙ 08/22/2019

Noise Flow: Noise Modeling with Conditional Normalizing Flows

Modeling and synthesizing image noise is an important aspect in many com...

Abdelrahman Abdelhamed, et al. ∙

research

∙ 01/17/2020

Adapting Grad-CAM for Embedding Networks

The gradient-weighted class activation mapping (Grad-CAM) method can fai...

Lei Chen, et al. ∙

research

∙ 06/07/2021

A Globally Normalized Neural Model for Semantic Parsing

In this paper, we propose a globally normalized model for context-free g...

Chenyang Huang, et al. ∙

research

∙ 07/05/2018

Scalable Recommender Systems through Recursive Evidence Chains

Recommender systems can be formulated as a matrix completion problem, pr...

Elias Tragas, et al. ∙

research

∙ 08/30/2018

A Coordinate-Free Construction of Scalable Natural Gradient

Most neural networks are trained using first-order optimization methods,...

Kevin Luk, et al. ∙

research

∙ 07/19/2019

Interactive Learning of Environment Dynamics for Sequential Tasks

In order for robots and other artificial agents to efficiently learn to ...

Robert Loftin, et al. ∙

research

∙ 07/26/2019

On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

How to best explore in domains with sparse, delayed, and deceptive rewar...

Chao Gao, et al. ∙

research

∙ 07/10/2019

Tails of Triangular Flows

Triangular maps are a construct in probability theory that allows the tr...

Priyank Jaini, et al. ∙

research

∙ 12/04/2021

Hierarchical Neural Data Synthesis for Semantic Parsing

Semantic parsing datasets are expensive to collect. Moreover, even the q...

Wei Yang, et al. ∙

research

∙ 04/03/2022

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Current language generation models suffer from issues such as repetition...

Kushal Arora, et al. ∙

research

∙ 04/10/2019

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

Safe reinforcement learning has many variants and it is still an open re...

Bilal Kartal, et al. ∙

research

∙ 05/10/2018

Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control

Reinforcement learning (RL) has had many successes in both "deep" and "s...

Kenny Young, et al. ∙

research

∙ 10/31/2018

Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds

In this paper, we investigate Dimensionality reduction (DR) maps in an i...

Kry Yik Chau Lui, et al. ∙

research

∙ 10/31/2018

A general system of differential equations to model first order adaptive algorithms

First order optimization algorithms play a major role in large scale mac...

André Belotto da Silva, et al. ∙

research

∙ 11/12/2018

Boosting Model Performance through Differentially Private Model Aggregation

A key factor in developing high performing machine learning models is th...

Sophia Collet, et al. ∙

research

∙ 11/30/2018

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

Deep reinforcement learning (DRL) has achieved great successes in recent...

Bilal Kartal, et al. ∙

research

∙ 12/03/2018

Few-Shot Self Reminder to Overcome Catastrophic Forgetting

Deep neural networks are known to suffer the catastrophic forgetting pro...

Junfeng Wen, et al. ∙

research

∙ 04/20/2019

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

The Pommerman Team Environment is a recently proposed benchmark which in...

Chao Gao, et al. ∙

research

∙ 05/28/2019

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer

In this work, we develop a novel regularizer to improve the learning of ...

Yanshuai Cao, et al. ∙

research

∙ 07/06/2019

Diachronic Embedding for Temporal Knowledge Graph Completion

Knowledge graphs (KGs) typically contain temporal facts indicating relat...

Rishab Goel, et al. ∙

research

∙ 07/22/2019

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

In this paper we explore how actor-critic methods in deep reinforcement ...

Pablo Hernandez-Leal, et al. ∙

research

∙ 07/24/2019

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...

Bilal Kartal, et al. ∙

research

∙ 07/25/2019

Action Guidance with MCTS for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...

Bilal Kartal, et al. ∙

research

∙ 08/25/2019

Normalizing Flows: Introduction and Ideas

Normalizing Flows are generative models which produce tractable distribu...

Ivan Kobyzev, et al. ∙

research

∙ 10/02/2019

Graph Generation with Variational Recurrent Neural Network

Generating graph structures is a challenging problem due to the diverse ...

Shih-Yang Su, et al. ∙

research

∙ 11/10/2019

Preventing Posterior Collapse in Sequence VAEs with Pooling

Variational Autoencoders (VAEs) hold great potential for modelling text,...

Teng Long, et al. ∙

research

∙ 01/28/2020

Unsupervised Multilingual Alignment using Wasserstein Barycenter

We study unsupervised multilingual alignment, the problem of finding wor...

Xin Lian, et al. ∙

research

∙ 04/22/2020

Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

Learning disentangled representations of real world data is a challengin...

Vikash Balasubramanian, et al. ∙

research

∙ 04/28/2020

Out-of-Sample Representation Learning for Multi-Relational Graphs

Many important problems can be formulated as reasoning in multi-relation...

Marjan Albooyeh, et al. ∙

research

∙ 03/17/2021

Learning Discriminative Prototypes with Dynamic Time Warping

Dynamic Time Warping (DTW) is widely used for temporal data processing. ...

Xiaobin Chang, et al. ∙

research

∙ 06/20/2021

Heterogeneous Multi-task Learning with Expert Diversity

Predicting multiple heterogeneous biological and medical targets is a ch...

Raquel Aoki, et al. ∙

research

∙ 06/20/2021

TD-GEN: Graph Generation With Tree Decomposition

We propose TD-GEN, a graph generation framework based on tree decomposit...

Hamed Shirzad, et al. ∙

research

∙ 03/31/2022

Scalable Whitebox Attacks on Tree-based Models

Adversarial robustness is one of the essential safety criteria for guara...

Giuseppe Castiglione, et al. ∙

research

∙ 06/08/2022

Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

The performance of time series forecasting has recently been greatly imp...

Amin Shabani, et al. ∙

research

∙ 07/07/2022

A Solver + Gradient Descent Training Algorithm for Deep Neural Networks

We present a novel hybrid algorithm for training Deep Neural Networks th...

Dhananjay Ashok, et al. ∙

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