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Found 10 papers, 6 papers with code
A Survey on Large Language Model-Based Game Agents
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI).
Training-Free Semantic Segmentation via LLM-Supervision
Additionally, we propose an assembly that merges the segmentation maps from the various subclass descriptors to ensure a more comprehensive representation of the different aspects in the test images.
Large Language Models Can Learn Temporal Reasoning
Instead of reasoning over the original context, we adopt a latent representation, temporal graph (TG) that facilitates the TR learning.
Causal-DFQ: Causality Guided Data-free Network Quantization
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study
Object tracking is an important functionality of edge video analytic systems and services.
Mitigating Group Bias in Federated Learning: Beyond Local Fairness
While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is privately stored on multiple clients.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Edge Security: Challenges and Issues
Edge computing is a paradigm that shifts data processing services to the network edge, where data are generated.
Parallel Detection for Efficient Video Analytics at the Edge
A common performance requirement in these mission-critical edge services is the near real-time latency of online object detection on edge devices.
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation.
