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Found 19 papers, 4 papers with code
Neural Conversational QA: Learning to Reason vs Exploiting Patterns
Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage.
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach.
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements.
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN).
Studying the Impact of Stochasticity on the Evaluation of Deep Neural Networks for Forest-Fire Prediction
We identify the Expected Calibration Error (ECE) as a robust metric that tests the proposed evaluation criteria, offering asymptotic guarantees of proper scoring rules and improved interpretability through calibration curves.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
Learning Representations on Logs for AIOps
Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults.
Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction
As an example, we consider a forest fire model where we aim to predict when a particular tree agent will start burning.
Unsupervised Hebbian Learning on Point Sets in StarCraft II
Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system.
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes
To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labeled source domain data.
Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services
We propose a combination of unsupervised and supervised model with minimum human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pretrained language model for artefact prediction on a large amount of conversation data.
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads.
FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud
The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably.
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc.
Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc.
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection
This paper proposes an end-to-end multi-task model for conversation modeling, which is optimized for two tasks, dialogue act prediction and response selection, with the latter being the task of interest.
Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns
On studying recent state-of-the-art models on the ShARCQA task, we found indications that the models learn spurious clues/patterns in the dataset.
Dialogue-act-driven Conversation Model : An Experimental Study
The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper.
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Dialogue Act recognition associate dialogue acts (i. e., semantic labels) to utterances in a conversation.
