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Found 22 papers, 1 papers with code
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets
Achieving both accuracy and diverse reasoning remains challenging for Large Language Models (LLMs) in complex domains like mathematics.
Enforcing Consistency and Fairness in Multi-level Hierarchical Classification with a Mask-based Output Layer
Traditional Multi-level Hierarchical Classification (MLHC) classifiers often rely on backbone models with $n$ independent output layers.
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented Generation Framework for Temporal Reasoning
Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge.
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification
Multi-level Hierarchical Classification (MLHC) tackles the challenge of categorizing items within a complex, multi-layered class structure.
Visual question answering: from early developments to recent advances -- a survey
Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation.
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset
Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning.
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e. g., Shumailov et al. (2023); Seddik et al. (2024)).
Falcon Mamba: The First Competitive Attention-free 7B Language Model
It is on par with Gemma 7B and outperforms models with different architecture designs, such as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B.
Alignment with Preference Optimization Is All You Need for LLM Safety
We demonstrate that preference optimization methods can effectively enhance LLM safety.
Falcon2-11B Technical Report
We introduce Falcon2-11B, a foundation model trained on over five trillion tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a vision-to-text model.
PORT: Preference Optimization on Reasoning Traces
This paper proposes using preference optimization methods on Chain-of-Thought steps in order to improve the reasoning performances of language models.
Data Quality in Edge Machine Learning: A State-of-the-Art Survey
From these observations, it follows that DQ research for edge ML is a critical and urgent exploration track for the safety and robust usefulness of present and future AI systems.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions
AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications.
Training Machine Learning models at the Edge: A Survey
This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available.
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques.
Practical Insights on Incremental Learning of New Human Physical Activity on the Edge
Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance.
Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks
Air Quality Monitoring and Forecasting has been a popular research topic in recent years.
Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models.
Regularization of the policy updates for stabilizing Mean Field Games
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+1
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge
Human activity recognition (HAR) has been a classic research problem.
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements.
