Search Results for author: Lovekesh Vig

Found 72 papers, 7 papers with code

Domain Adaptation for NMT via Filtered Iterative Back-Translation

no code implementations EACL (AdaptNLP) 2021 Surabhi Kumari, Nikhil Jaiswal, Mayur Patidar, Manasi Patwardhan, Shirish Karande, Puneet Agarwal, Lovekesh Vig

In comparison, in this work, we observe that a simpler filtering approach based on a domain classifier, applied only to the pseudo-training data can consistently perform better, providing performance gains of 1. 40, 1. 82 and 0. 76 in terms of BLEU score for Medical, Law and IT in one direction, and 1. 28, 1. 60 and 1. 60 in the other direction in low resource scenario over competitive baselines.

Domain Adaptation Machine Translation +2

Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering

no code implementations NAACL 2022 Rajat Kumar, Mayur Patidar, Vaibhav Varshney, Lovekesh Vig, Gautam Shroff

However, even skilled domain experts are often unable to foresee all possible user intents at design time and for practical applications, novel intents may have to be inferred incrementally on-the-fly from user utterances.

Clustering Intent Detection +4

Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection

no code implementations Findings (NAACL) 2022 Vaibhav Varshney, Mayur Patidar, Rajat Kumar, Lovekesh Vig, Gautam Shroff

This typically entails repeated retraining of the intent detector on both the existing and novel intents which can be expensive and would require storage of all past data corresponding to prior intents.

Continual Learning Contrastive Learning +2

Can Physics Informed Neural Operators Self Improve?

no code implementations23 Nov 2023 Ritam Majumdar, Amey Varhade, Shirish Karande, Lovekesh Vig

Physics Informed Neural Operators (PINO) overcome this constraint by utilizing a physics loss for the training, however the accuracy of PINO trained without data does not match the performance obtained by training with data.

HyperLoRA for PDEs

no code implementations18 Aug 2023 Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations.

Meta-Learning regression

How important are specialized transforms in Neural Operators?

no code implementations18 Aug 2023 Ritam Majumdar, Shirish Karande, Lovekesh Vig

Simulating physical systems using Partial Differential Equations (PDEs) has become an indispensible part of modern industrial process optimization.

Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation

no code implementations26 Apr 2023 Krishnam Hasija, Shrishti Pradhan, Manasi Patwardhan, Raveendra Kumar Medicherla, Lovekesh Vig, Ravindra Naik

We further fine-tune UnixCoder, the best-performing model for zero-shot cross-programming language code search, for the Code Cloning task with the SBT IRs of C code-pairs, available in the CodeNet dataset.

C++ code Code Search

DeepEpiSolver: Unravelling Inverse problems in Covid, HIV, Ebola and Disease Transmission

no code implementations24 Mar 2023 Ritam Majumdar, Shirish Karande, Lovekesh Vig

We then use a neural network to learn the mapping between spread trajectories and coefficients of SIDR in an offline manner.

Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language

no code implementations16 Mar 2023 Ankita Sontakke, Kanika Kalra, Manasi Patwardhan, Lovekesh Vig, Raveendra Kumar Medicherla, Ravindra Naik, Shrishti Pradhan

In this paper, we focus on transferring the knowledge acquired by the code-to-pseudocode neural model trained on a high resource PL (C++) using parallel code-pseudocode data.

Code Generation Transfer Learning +1

Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

1 code implementation20 Feb 2023 Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan

To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas.

Image Classification Multiple Instance Learning +1

Neuro-symbolic Meta Reinforcement Learning for Trading

no code implementations15 Jan 2023 S I Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig

We model short-duration (e. g. day) trading in financial markets as a sequential decision-making problem under uncertainty, with the added complication of continual concept-drift.

Decision Making Meta Reinforcement Learning +3

Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning

no code implementations20 Dec 2022 Ramya Hebbalaguppe, Rishabh Patra, Tirtharaj Dash, Gautam Shroff, Lovekesh Vig

Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples.

Image Classification Test

Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs

no code implementations20 Dec 2022 Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana

We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants.

Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions

no code implementations20 Dec 2022 Mayur Patidar, Prayushi Faldu, Avinash Singh, Lovekesh Vig, Indrajit Bhattacharya, Mausam

When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable.

Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss

no code implementations29 Nov 2022 Vedant Shah, Aditya Agrawal, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Tanmay Verlekar

Real data is often imperfect meaning that even if the symbolic theory remains unchanged, we may still need to address the problem of mapping raw data to high-level symbols, each time there is a change in the data acquisition environment or equipment.

Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces

no code implementations19 Sep 2022 Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan

However, connectionist models struggle to include explicit domain knowledge for deductive reasoning.

An Efficient Anchor-free Universal Lesion Detection in CT-scans

no code implementations30 Mar 2022 Manu Sheoran, Meghal Dani, Monika Sharma, Lovekesh Vig

Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions.

Lesion Detection Medical Object Detection

DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection

no code implementations British Machine Vision Conference 2021 Manu Sheoran, Meghal Dani, Monika Sharma, Lovekesh Vig

In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion.

Computed Tomography (CT) Lesion Detection +2

TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words

no code implementations14 Mar 2022 Arushi Jain, Shubham Paliwal, Monika Sharma, Lovekesh Vig

In this paper, we propose to train a deep network to capture the spatial associations between different word pairs present in the table image for unravelling the table structure.

Text Detection

Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions

no code implementations14 Mar 2022 Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks.

Activity Recognition Continual Learning +2

Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression

no code implementations25 Feb 2022 Diksha Garg, Pankaj Malhotra, Anil Bhatia, Sanjay Bhat, Lovekesh Vig, Gautam Shroff

We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC).


Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

no code implementations11 Feb 2022 Jyoti Narwariya, Chetan Verma, Pankaj Malhotra, Lovekesh Vig, Easwara Subramanian, Sanjay Bhat

One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the overall electricity procurement in the wholesale markets is minimized, e. g. it is desirable that consumers consume less during peak hours when cost of procurement for brokers from wholesale markets are high.


DRTCI: Learning Disentangled Representations for Temporal Causal Inference

no code implementations20 Jan 2022 Garima Gupta, Lovekesh Vig, Gautam Shroff

Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment and the patient outcome.

Causal Inference counterfactual +1

Solving Visual Analogies Using Neural Algorithmic Reasoning

no code implementations19 Nov 2021 Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash

We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs.

Program Synthesis Visual Analogies

PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation

no code implementations31 Oct 2021 Mrinal Rawat, Ramya Hebbalaguppe, Lovekesh Vig

While Out-of-distribution (OOD) detection has been well explored in computer vision, there have been relatively few prior attempts in OOD detection for NLP classification.

Data Augmentation Language Modelling +4

OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN

no code implementations8 Sep 2021 Shubham Paliwal, Monika Sharma, Lovekesh Vig

The proposed pipeline, named OSSR-PID, is fast and gives outstanding performance for recognition of symbols on a synthetic dataset of 100 P&ID diagrams.

One-Shot Learning

Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams

no code implementations8 Sep 2021 Shubham Paliwal, Arushi Jain, Monika Sharma, Lovekesh Vig

A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper.

Line Detection Management

CAMTA: Causal Attention Model for Multi-touch Attribution

no code implementations21 Dec 2020 Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff

Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc.

Selection bias

Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays

no code implementations19 Dec 2020 Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, Ashwin Srinivasan

In this paper, deep neural networks are used to extract domain-specific features(morphological features like ground-glass opacity and disease indications like pneumonia) directly from the image data.

COVID-19 Diagnosis

Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation

no code implementations16 Dec 2020 Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning.

Distributional Reinforcement Learning Offline RL +3

Incorporating Symbolic Domain Knowledge into Graph Neural Networks

2 code implementations23 Oct 2020 Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig

These kinds of problems have been addressed effectively in the past by Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations.

Inductive logic programming

Handling Variable-Dimensional Time Series with Graph Neural Networks

no code implementations1 Jul 2020 Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

Such a combinatorial generalization is achieved by conditioning the layers of a core neural network-based time series model with a "conditioning vector" that carries information of the available combination of sensors for each time series.

Activity Recognition Time Series +2

Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

no code implementations30 Jun 2020 Jyoti Narwariya, Pankaj Malhotra, Vishnu Tv, Lovekesh Vig, Gautam Shroff

Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure.

Time Series Time Series Analysis

MultiMBNN: Matched and Balanced Causal Inference with Neural Networks

no code implementations28 Apr 2020 Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff

Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc.

Causal Inference

TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images

5 code implementations6 Jan 2020 Shubham Paliwal, Vishwanath D, Rohit Rahul, Monika Sharma, Lovekesh Vig

This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table.

Table Detection Transfer Learning

MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population

no code implementations9 Dec 2019 Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff

Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc.

Causal Inference counterfactual +2

ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks

no code implementations21 Nov 2019 Monika Sharma, Shikha Gupta, Arindam Chowdhury, Lovekesh Vig

To this end, we formulate the problem of reasoning over statistical charts as a classification task using MAC-Networks to give answers from a predefined vocabulary of generic answers.

General Classification Test +1

Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction

no code implementations14 Nov 2019 Kushagra Mahajan, Monika Sharma, Lovekesh Vig

The algorithm is both fast and accurate and utilizes a standard Optical character recognition (OCR) engine such as Tesseract to find character based unambiguous keypoints, which are utilized to identify precise keypoint correspondences between two images.

Homography Estimation Optical Character Recognition +2

From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training

no code implementations WS 2019 Mayur Patidar, Surabhi Kumari, Manasi Patwardhan, Kar, Shirish e, Puneet Agarwal, Lovekesh Vig, Gautam Shroff

We observe that the proposed approach provides consistent gains in the performance of BERT for multiple benchmark datasets (e. g. 1. 0{\%} gain on MLDocs, and 1. 2{\%} gain on XNLI over translate-train with BERT), while requiring a single model for multiple languages.

Cross-Lingual Transfer Test

Meta-Learning for Few-Shot Time Series Classification

no code implementations13 Sep 2019 Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Vishnu Tv

We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks.

Activity Recognition Classification +5

NISER: Normalized Item and Session Representations to Handle Popularity Bias

2 code implementations10 Sep 2019 Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii.

Session-Based Recommendations

Meta-Learning for Black-box Optimization

no code implementations16 Jul 2019 Vishnu TV, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff

Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization.


One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis

no code implementations6 Jun 2019 Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul

Our interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc.

Program Synthesis Test

ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification

no code implementations29 Apr 2019 Kathan Kashiparekh, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

We also provide qualitative insights into the working of CTN by: i) analyzing the activations and filters of first convolution layer suggesting the filters in CTN are generically useful, ii) analyzing the impact of the design decision to incorporate multiple length decisions, and iii) finding regions of time series that affect the final classification decision via occlusion sensitivity analysis.

Computational Efficiency General Classification +3

Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks

no code implementations1 Apr 2019 Priyanka Gupta, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff

We, therefore, conclude that pre-trained deep models like TimeNet and HealthNet allow leveraging the advantages of deep learning for clinical time series analysis tasks, while also minimize dependence on hand-crafted features, deal robustly with scarce labeled training data scenarios without overfitting, as well as reduce dependence on expertise and resources required to train deep networks from scratch.

Domain Adaptation Time Series +2

Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models

no code implementations23 Mar 2019 Vishnu TV, Diksha, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings.

regression Time Series Analysis +1

Automatic Information Extraction from Piping and Instrumentation Diagrams

no code implementations28 Jan 2019 Rohit Rahul, Shubham Paliwal, Monika Sharma, Lovekesh Vig

To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols.


Learning to Clean: A GAN Perspective

no code implementations28 Jan 2019 Monika Sharma, Abhishek Verma, Lovekesh Vig

We compare the performance of CycleGAN for document cleaning tasks using unpaired images with a Conditional GAN trained on paired data from the same dataset.

Denoising Image-to-Image Translation +1

MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling

no code implementations28 Dec 2018 Vishwanath D, Lovekesh Vig, Gautam Shroff, Puneet Agarwal

In this paper we present Meeting Bot, a reinforcement learning based conversational system that interacts with multiple users to schedule meetings.

reinforcement-learning Reinforcement Learning (RL) +1

Deep Reader: Information extraction from Document images via relation extraction and Natural Language

no code implementations11 Dec 2018 Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan

In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image.

Optical Character Recognition Optical Character Recognition (OCR) +2

An Efficient End-to-End Neural Model for Handwritten Text Recognition

no code implementations20 Jul 2018 Arindam Chowdhury, Lovekesh Vig

Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports.

Handwritten Text Recognition

Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks

no code implementations4 Jul 2018 Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data.

Mortality Prediction Time Series +2

Logical Explanations for Deep Relational Machines Using Relevance Information

no code implementations2 Jul 2018 Ashwin Srinivasan, Lovekesh Vig, Michael Bain

We investigate the use of a Bayes-like approach to identify logical proxies for local predictions of a DRM.

Inductive logic programming

Information Bottleneck Inspired Method For Chat Text Segmentation

no code implementations IJCNLP 2017 S Vishal, Mohit Yadav, Lovekesh Vig, Gautam Shroff

We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints.

Representation Learning Text Generation +2

Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

no code implementations4 Sep 2017 Narendhar Gugulothu, Vishnu Tv, Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, Gautam Shroff

We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values.

Time Series Time Series Analysis

TimeNet: Pre-trained deep recurrent neural network for time series classification

2 code implementations23 Jun 2017 Pankaj Malhotra, Vishnu Tv, Lovekesh Vig, Puneet Agarwal, Gautam Shroff

Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.

Dynamic Time Warping General Classification +3

Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

no code implementations EACL 2017 Mohit Yadav, Lovekesh Vig, Gautam Shroff

Motivated by these practical issues, we propose a novel curriculum inspired training procedure for Memory Networks to improve the performance for machine comprehension with relatively small volumes of training data.

Question Answering Reading Comprehension +1

Deep Convolutional Neural Networks for Pairwise Causality

no code implementations3 Jan 2017 Karamjit Singh, Garima Gupta, Lovekesh Vig, Gautam Shroff, Puneet Agarwal

Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning.

Attribute Causal Discovery +2

Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs

no code implementations20 Dec 2016 Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat

We investigate solving discrete optimisation problems using the estimation of distribution (EDA) approach via a novel combination of deep belief networks(DBN) and inductive logic programming (ILP). While DBNs are used to learn the structure of successively better feasible solutions, ILP enables the incorporation of domain-based background knowledge related to the goodness of solutions. Recent work showed that ILP could be an effective way to use domain knowledge in an EDA scenario. However, in a purely ILP-based EDA, sampling successive populations is either inefficient or not straightforward. In our Neuro-symbolic EDA, an ILP engine is used to construct a model for good solutions using domain-based background knowledge. These rules are introduced as Boolean features in the last hidden layer of DBNs used for EDA-based optimization. This incorporation of logical ILP features requires some changes while training and sampling from DBNs: (a)our DBNs need to be trained with data for units at the input layer as well as some units in an otherwise hidden layer, and (b)we would like the samples generated to be drawn from instances entailed by the logical model. We demonstrate the viability of our approach on instances of two optimisation problems: predicting optimal depth-of-win for the KRK endgame, and jobshop scheduling. Our results are promising: (i)On each iteration of distribution estimation, samples obtained with an ILP-assisted DBN have a substantially greater proportion of good solutions than samples generated using a DBN without ILP features, and (ii)On termination of distribution estimation, samples obtained using an ILP-assisted DBN contain more near-optimal samples than samples from a DBN without ILP features. These results suggest that the use of ILP-constructed theories could be useful for incorporating complex domain-knowledge into deep models for estimation of distribution based procedures.

Inductive logic programming

Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

no code implementations22 Aug 2016 Pankaj Malhotra, Vishnu Tv, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff

Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e. g., exponential degradation.

Exponential degradation Time Series +1

Generation of Near-Optimal Solutions Using ILP-Guided Sampling

no code implementations3 Aug 2016 Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Puneet Agarwal

To answer this in the affirmative, we need: (a)a general-purpose technique for the incorporation of domain knowledge when constructing models for optimal values; and (b)a way of using these models to generate new data samples.

Inductive logic programming Job Shop Scheduling +1

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

9 code implementations1 Jul 2016 Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.

Anomaly Detection Outlier Detection +3

ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines

no code implementations5 May 2016 Mohit Yadav, Pankaj Malhotra, Lovekesh Vig, K Sriram, Gautam Shroff

The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset.

Anomaly Detection Time Series +1

Long Short Term Memory Networks for Anomaly Detection in Time Series

1 code implementation ESANN 2015 Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal

Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long term memory.

Anomaly Detection Fault Detection +2

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