Oceania
Federated Graph Learning for Cross-Domain Recommendation Ziqi Y ang
Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings.
Test-Time Distribution Normalization for Contrastively Learned Vision-language Models Yifei Zhou
This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time.
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (V AD) for robotic power line inspection.
DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning Hengli Li
Along with the dataset, two tasks are proposed to evaluate machines'
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
Top-down Bird's Eye View (BEV) maps are a popular perceptual representation for ground robot navigation due to their richness and flexibility for downstream
A Benchmark for Evaluating Language Model Fit
Implicitly or explicitly, this data is composed of domains--varying distributions of language.
A Evaluation Tasks 450 Table 4 lists the tasks in our evaluation, and examples from each task
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Provable Partially Observable Reinforcement Learning with Privileged Information Yang Cai
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).