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Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays
In this paper, our focus is on constructing models to assist a clinician...
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Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
Most of the existing deep reinforcement learning (RL) approaches for ses...
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Incorporating Symbolic Domain Knowledge into Graph Neural Networks
Our interest is in scientific problems with the following characteristic...
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Hi-CI: Deep Causal Inference in High Dimensions
We address the problem of counterfactual regression using causal inferen...
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Handling Variable-Dimensional Time Series with Graph Neural Networks
Several applications of Internet of Things(IoT) technology involve captu...
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Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
Automated equipment health monitoring from streaming multisensor time-se...
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MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
Causal inference (CI) in observational studies has received a lot of att...
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TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images
With the widespread use of mobile phones and scanners to photograph and ...
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MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Performing inference on data obtained through observational studies is b...
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ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks
Despite the improvements in perception accuracies brought about via deep...
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Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction
Precise homography estimation between multiple images is a pre-requisite...
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Meta-Learning for Few-Shot Time Series Classification
Deep neural networks (DNNs) have achieved state-of-the-art results on ti...
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NISER: Normalized Item and Session Representations with Graph Neural Networks
The goal of session-based recommendation (SR) models is to utilize the i...
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Meta-Learning for Black-box Optimization
Recently, neural networks trained as optimizers under the "learning to l...
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One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis
Our interest in this paper is in meeting a rapidly growing industrial de...
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ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
Training deep neural networks often requires careful hyper-parameter tun...
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Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
Deep neural networks have shown promising results for various clinical p...
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Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor ...
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Learning to Clean: A GAN Perspective
In the big data era, the impetus to digitize the vast reservoirs of data...
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Automatic Information Extraction from Piping and Instrumentation Diagrams
One of the most common modes of representing engineering schematics are ...
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MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling
In this paper we present Meeting Bot, a reinforcement learning based con...
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Reading Industrial Inspection Sheets by Inferring Visual Relations
The traditional mode of recording faults in heavy factory equipment has ...
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Deep Reader: Information extraction from Document images via relation extraction and Natural Language
Recent advancements in the area of Computer Vision with state-of-art Neu...
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Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning
We present an effective technique for training deep learning agents capa...
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An Efficient End-to-End Neural Model for Handwritten Text Recognition
Offline handwritten text recognition from images is an important problem...
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Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
Deep neural networks have shown promising results for various clinical p...
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Logical Explanations for Deep Relational Machines Using Relevance Information
Our interest in this paper is in the construction of symbolic explanatio...
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Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
We investigate solving discrete optimisation problems using the estimati...
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Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Our interest in this paper is in optimisation problems that are intracta...
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LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc., are typic...
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