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Found 53 papers, 4 papers with code
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection
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.
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering
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.
Ranked #1 on
Open Intent Discovery
on BANKING77
Acceleron: A Tool to Accelerate Research Ideation
To aid with research ideation, we propose `Acceleron', a research accelerator for different phases of the research life cycle, and which is specially designed to aid the ideation process.
Conservative Predictions on Noisy Financial Data
We apply a similar approach, where a model abstains from making a prediction on data points that it is uncertain on.
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting
Cross-domain and cross-compositional generalization of Text-to-SQL semantic parsing is a challenging task.
Neuro-symbolic Meta Reinforcement Learning for Trading
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.
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
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.
Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss
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
However, connectionist models struggle to include explicit domain knowledge for deductive reasoning.
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
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.
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
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).
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
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.
Solving Visual Analogies Using Neural Algorithmic Reasoning
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.
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!
We benchmark the baseline techniques on this synthetic data as well as use it for data augmentation.
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems
The ability to solve Bongard problems is an example of such a test.
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.
CAMTA: Causal Attention Model for Multi-touch Attribution
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.
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
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.
Hi-CI: Deep Causal Inference in High Dimensions
The proposed architecture comprises of a decorrelation network and an outcome prediction network.
Handling Variable-Dimensional Time Series with Graph Neural Networks
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.
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
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.
Privacy Guidelines for Contact Tracing Applications
Due to this there is an emergence of mobile based applications for contact tracing.
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc.
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
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.
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.
Meta-Learning for Few-Shot Time Series Classification
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.
NISER: Normalized Item and Session Representations to Handle Popularity Bias
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.
Ranked #2 on
Session-Based Recommendations
on Last.FM
Meta-Learning for Black-box Optimization
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
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.
Fast Online "Next Best Offers" using Deep Learning
In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting.
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
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.
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
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.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
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.
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.
Deep Reader: Information extraction from Document images via relation extraction and Natural Language
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
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning
We further train a meta agent with a mixture of behaviors by learning an ensemble of different models using reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
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.
Information Bottleneck Inspired Method For Chat Text Segmentation
We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints.
Crop Planning using Stochastic Visual Optimization
Given the weather and soil properties, farmers need to take critical decisions such as which seed variety to plant and in what proportion, in order to maximize productivity.
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.
Comparative Benchmarking of Causal Discovery Techniques
In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference.
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.
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension
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.
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.
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines
The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset.
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.
