New Framework

Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.

Papers

April 7, 2022

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies
Duo Xu, Faramarz Fekri
Deep Reinforcement Learning New Framework Temporal Logic Hierarchical Reinforcement Learning Inductive Logic Programming Underlying Causal Transition Model

April 5, 2022

Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks
Tristan Thrush, Kushal Tirumala, Anmol Gupta, Max Bartolo, Pedro Rodriguez, Tariq Kane, William Gaviria Rojas, Peter Mattson, Adina Williams, Douwe Kiela
New Framework Multi Task NLP Task Related Task NLP Model

April 4, 2022

Signal Propagation: A Framework for Learning and Inference In a Forward Pass
Adam Kohan, Edward A. Rietman, Hava T. Siegelmann
LeArning Abstract New Framework Scientific Inference Back Propagation Propagation Environment Forward Pas Backward Learning Learning Signal Hardware Efficient

March 30, 2022

Artificial Intelligence: Framework of driving triggers to past, present and future applications and influencers of industry sector adoption
Richard Fulton, Diane Fulton, Susan Kaplan
Artificial Intelligence New Framework Technology Information Data Natural Trigger Disruptive Technology Future Application

March 29, 2022

March 28, 2022

A Framework of Meta Functional Learning for Regularising Knowledge Transfer
Pan Li, Yanwei Fu, Shaogang Gong
New Framework Knowledge Transfer Logic Regularization Functional Learning Functional Regularization Discriminant Function

March 26, 2022

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns
Daniel Reißner, Abel Armas-Cervantes, Marcello La Rosa
Strong Generalization New Framework Process Mining Supervised Baseline Process Model Process Discovery Log Pattern Generalization Measure

March 25, 2022

Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification
Sohini Roychowdhury
New Framework Semi Supervised Scene Understanding Video Classification Video Data Scene Classification Pre Trained Object Key Frame

March 23, 2022

March 22, 2022

ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Jinze Chen, Yang Wang, Yang Cao, Feng Wu, Zheng-Jun Zha
New Framework Motion Estimation Dynamic Vision Sensor Motion Segmentation Color Image Denoising

March 18, 2022

FORCE: A Framework of Rule-Based Conversational Recommender System
Jun Quan, Ze Wei, Qiang Gan, Jingqi Yao, Jingyi Lu, Yuchen Dong, Yiming Liu, Yi Zeng, Chao Zhang, Yongzhi Li, Huang Hu, Yingying He, Yang Yang, Daxin Jiang
Deep Learning Model New Framework External Human Force Bot Account Conversational Recommender System

March 17, 2022

March 16, 2022

Instance-level loss based multiple-instance learning framework for acoustic scene classification
Won-Gook Choi, Joon-Hyuk Chang, Jae-Mo Yang, Han-Gil Moon
New Framework Multiple Instance Learning Convolutional Filter Acoustic Scene Classification Acoustic Scene Multiple Instance Instance Level Label Instance Level Loss

March 15, 2022

March 14, 2022

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Machine Learning New Framework Power System Trustworthy Machine Learning Decarbonization Study Wind Farm Power Transformer

New Framework

Papers

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies

Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

Signal Propagation: A Framework for Learning and Inference In a Forward Pass

Artificial Intelligence: Framework of driving triggers to past, present and future applications and influencers of industry sector adoption

A Framework for Improving the Reliability of Black-box Variational Inference

ReIL: A Framework for Reinforced Intervention-based Imitation Learning

A Framework of Meta Functional Learning for Regularising Knowledge Transfer

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification

A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors

A Framework for Fast Polarity Labelling of Massive Data Streams

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation

FORCE: A Framework of Rule-Based Conversational Recommender System

SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data

A Framework and Benchmark for Deep Batch Active Learning for Regression

Instance-level loss based multiple-instance learning framework for acoustic scene classification

Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps

A Framework for Verifiable and Auditable Federated Anomaly Detection

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems