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Found 14 papers, 3 papers with code
Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection
However, existing approaches depend on static anomaly prompts that are prone to cross-semantic ambiguity, and prioritize global image-level representations over crucial local pixel-level image-to-text alignment that is necessary for accurate anomaly localization.
Anytime Neural Architecture Search on Tabular Data
This transition demands an efficient and responsive anytime NAS approach that is capable of returning current optimal architectures within any given time budget while progressively enhancing architecture quality with increased budget allocation.
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection
Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively.
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
Recent years have witnessed a surging interest in Neural Architecture Search (NAS).
ARM-Net: Adaptive Relation Modeling Network for Structured Data
The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
Privacy Preserving Vertical Federated Learning for Tree-based Models
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other.
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions.
GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
Predicting the future paths of an agent's neighbors accurately and in a timely manner is central to the autonomous applications for collision avoidance.
ISBNet: Instance-aware Selective Branching Networks
Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search (NAS).
Understanding Architectures Learnt by Cell-based Neural Architecture Search
Neural architecture search (NAS) searches architectures automatically for given tasks, e. g., image classification and language modeling.
The Disruptions of 5G on Data-driven Technologies and Applications
With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management.
Networking and Internet Architecture Databases Distributed, Parallel, and Cluster Computing
Dynamic Routing Networks
The deployment of deep neural networks in real-world applications is mostly restricted by their high inference costs.
Effective and Efficient Dropout for Deep Convolutional Neural Networks
However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs.
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
Model slicing could be viewed as an elastic computation solution without requiring more computational resources.
