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Found 15 papers, 4 papers with code
Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations
To overcome these limitations, we propose Graph-Grounded LLMs, a system that improves LLM performance on graph-related tasks by integrating a graph library through function calls.
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks
To address these challenges, we introduce a novel framework, GFlowVLM, a framework that fine-tune VLMs using Generative Flow Networks (GFlowNets) to promote generation of diverse solutions for complex reasoning tasks.
Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees
We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot.
Fast Inventory for 3GPP Ambient IoT Considering Device Unavailability due to Energy Harvesting
With the growing demand for massive internet of things (IoT), new IoT technology, namely ambient IoT (A-IoT), has been studied in the 3rd Generation Partnership Project (3GPP).
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation
Real-world driving involves intricate interactions among vehicles navigating through dense traffic scenarios.
SARC: Soft Actor Retrospective Critic
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning
During exploration, DSEE explores the environment and updates the estimates for expected reward and transition probabilities.
Information-theoretic Evolution of Model Agnostic Global Explanations
Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior.
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance
Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains.
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)
Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability.
ShapeVis: High-dimensional Data Visualization at Scale
This graph captures the structural characteristics of the point cloud, and its weights are determined using a Finite Markov Chain.
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches.
MAGIX: Model Agnostic Globally Interpretable Explanations
Explaining the behavior of a black box machine learning model at the instance level is useful for building trust.
