South America
G3: AnEffectiveandAdaptiveFrameworkfor WorldwideGeolocalizationUsingLarge Multi-ModalityModels
As a result, existing studies have clear limitations whenscaledtoaworldwidecontext.
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models Lucas N. Alegre 1, 2 Ana L. C. Bazzan 1 Ann Now รฉ 2 Bruno C. da Silva 3 1
We introduce a principled method for performing zero-shot transfer in reinforcement learning (RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has been investigated by leveraging methods rooted in generalized policy improvement (GPI) and successor features (SFs).
STREETS: A Novel Camera Network Dataset for Traffic Flow
We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
We give a model of how to infer natural language rules by doing experiments.
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
Neural Information Processing Systems http://nips.cc/
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
We contend that utilizing existing relational databases is a promising approach to construct a benchmark that has both merits.
PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Neural Information Processing Systems http://nips.cc/
Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model
However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (edges) and node information (attributes) can be jointly leveraged to design high-performance clustering algorithms. Under a general model for the network and node attributes, this work establishes an information-theoretic criterion for the exact recovery of community labels and characterizes a phase transition determined by the Chernoff-Hellinger divergence of the model.
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
Neural Information Processing Systems http://nips.cc/