Lance Kaplan | DeepAI

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Chao Zhang
239 publications
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∙ 06/21/2022

Automatic Concept Extraction for Concept Bottleneck-based Video Classification

Recent efforts in interpretable deep learning models have shown that con...

0 Jeya Vikranth Jeyakumar, et al. ∙

∙ 10/15/2021

Using DeepProbLog to perform Complex Event Processing on an Audio Stream

In this paper, we present an approach to Complex Event Processing (CEP) ...

0 Marc Roig Vilamala, et al. ∙

∙ 12/09/2020

NSL: Hybrid Interpretable Learning From Noisy Raw Data

Inductive Logic Programming (ILP) systems learn generalised, interpretab...

0 Daniel Cunnington, et al. ∙

∙ 11/20/2020

A General Framework for Distributed Inference with Uncertain Models

This paper studies the problem of distributed classification with a netw...

3 James Z. Hare, et al. ∙

∙ 09/07/2020

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

Training a model to detect patterns of interrelated events that form sit...

0 Marc Roig Vilamala, et al. ∙

∙ 06/07/2020

Uncertainty-Aware Deep Classifiers using Generative Models

Deep neural networks are often ignorant about what they do not know and ...

0 Murat Sensoy, et al. ∙

∙ 11/04/2019

Spherical Text Embedding

Unsupervised text embedding has shown great power in a wide range of NLP...

0 Yu Meng, et al. ∙

∙ 10/24/2019

Non-Bayesian Social Learning with Gaussian Uncertain Models

Non-Bayesian social learning theory provides a framework for distributed...

0 James Z. Hare, et al. ∙

∙ 10/15/2019

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

Traditional deep neural nets (NNs) have shown the state-of-the-art perfo...

0 Xujiang Zhao, et al. ∙

∙ 09/10/2019

Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs

We study the problem of non-Bayesian social learning with uncertain mode...

0 César A. Uribe, et al. ∙

∙ 09/09/2019

Non-Bayesian Social Learning with Uncertain Models

Non-Bayesian social learning theory provides a framework that models dis...

0 James Z. Hare, et al. ∙

∙ 07/05/2019

Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units

In this paper, we introduce the concept of Prior Activation Distribution...

0 Lakmal Meegahapola, et al. ∙

∙ 09/20/2018

Probabilistic Logic Programming with Beta-Distributed Random Variables

We enable aProbLog---a probabilistic logical programming approach---to r...

0 Federico Cerutti, et al. ∙

∙ 09/20/2018

Uncertainty Aware AI ML: Why and How

This paper argues the need for research to realize uncertainty-aware art...

0 Lance Kaplan, et al. ∙

∙ 06/05/2018

Evidential Deep Learning to Quantify Classification Uncertainty

Deterministic neural nets have been shown to learn effective predictors ...

0 Murat Sensoy, et al. ∙

∙ 03/05/2018

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Heterogeneous information networks (HINs) are ubiquitous in real-world a...

0 Yu Shi, et al. ∙

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