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Found 16 papers, 3 papers with code
Improving NMT via Filtered Back Translation
This is further followed by fine-tuning on the domain-specific corpus.
Domain Adaptation for NMT via Filtered Iterative Back-Translation
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.
Complex Question Answering on knowledge graphs using machine translation and multi-task learning
Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG.
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training
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.
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling
In this paper we present Meeting Bot, a reinforcement learning based conversational system that interacts with multiple users to schedule meetings.
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
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.
TimeNet: Pre-trained deep recurrent neural network for time series classification
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.
Deep Convolutional Neural Networks for Pairwise Causality
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning.
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
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.
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
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.
Generation of Near-Optimal Solutions Using ILP-Guided Sampling
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.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
Long Short Term Memory Networks for Anomaly Detection in Time Series
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.
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data
While analyzing vehicular sensor data, we found that frequently occurring waveforms could serve as features for further analysis, such as rule mining, classification, and anomaly detection.
Warranty Cost Estimation Using Bayesian Network
All multi-component product manufacturing companies face the problem of warranty cost estimation.
Multi-Sensor Event Detection using Shape Histograms
We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor.
