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Found 24 papers, 13 papers with code
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs
Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation.
Decision Theory for Treatment Choice Problems with Partial Identification
We apply classical statistical decision theory to a large class of treatment choice problems with partial identification, revealing important theoretical and practical challenges but also interesting research opportunities.
Feasibility Conditions for Mobile LiFi
Then, we derive the expression of LiFi transmission rate based on the m-pulse-amplitude-modulation (M-PAM).
Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data
In this work, we propose SWSA (Selection With Synthetic Anomalies): a general-purpose framework to select image-based anomaly detectors with a generated synthetic validation set.
Treatment Choice, Mean Square Regret and Partial Identification
We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data.
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Prompt learning for vision-language models, e. g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network
Firstly, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, so as to maximize the minimum amount of data uploaded by the sensor.
The Role of Data Curation in Image Captioning
Image captioning models are typically trained by treating all samples equally, neglecting to account for mismatched or otherwise difficult data points.
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection.
Zero-Shot Anomaly Detection via Batch Normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
Ranked #1 on
Unsupervised Anomaly Detection
on AnoShift
Unsupervised Anomaly Detection
zero-shot anomaly detection
+1
Intelligent Reflecting Surface assisted Integrated Sensing and Communication System
This paper proves the efficiency of IRS enabled ISAC system, which motivates the implementation of IRS to enhance the sensing capability in ISAC system.
Multilingual Multimodal Learning with Machine Translated Text
We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model.
Zero-Shot Cross-Lingual Image-to-Text Retrieval
Zero-Shot Cross-Lingual Text-to-Image Retrieval
+3
Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links.
Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review
The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.
Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks.
Treatment Choice with Nonlinear Regret
The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty.
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Switching Recurrent Kalman Networks
In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance.
On Language Models for Creoles
Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature.
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e. g. rotations, reflections, and cropping) play an important role in self-supervised learning.
Variational Dynamic Mixtures
Deep probabilistic time series forecasting models have become an integral part of machine learning.
Learning Topometric Semantic Maps from Occupancy Grids
Today's mobile robots are expected to operate in complex environments they share with humans.
Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples.
