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Found 22 papers, 11 papers with code
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web.
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification
At the local level, the WSI sequence is divided into multiple subsequences.
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing.
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
Towards the Universal Defense for Query-Based Audio Adversarial Attacks
In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.
Towards the Transferable Audio Adversarial Attack via Ensemble Methods
In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition.
Introduction of an intriguing approach for eletric current transformer on-site examining repairing
However, due to the frequent occurrence of full current passing through the current transformer during use, its secondary winding has relatively more turns.
Self-Supervised Feature Learning for Long-Term Metric Visual Localization
Visual localization is the task of estimating camera pose in a known scene, which is an essential problem in robotics and computer vision.
Multilingual Relation Classification via Efficient and Effective Prompting
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes.
SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos
One of the key reasons for this is that sampling pairs of similar video clips, a required step for many self-supervised contrastive learning methods, is currently done conservatively to avoid false positives.
Ranked #1 on
Self-Supervised Action Recognition
on UCF101
(Pre-Training Dataset metric)
Why only Micro-F1? Class Weighting of Measures for Relation Classification
Relation classification models are conventionally evaluated using only a single measure, e. g., micro-F1, macro-F1 or AUC.
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data.
Contrastive Learning
Low Resource Named Entity Recognition
+4
Efficient Equivariant Network
Group equivariant CNNs (G-CNNs) that incorporate more equivariance can significantly improve the performance of conventional CNNs.
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
The goal of few-shot classification is to classify new categories with few labeled examples within each class.
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e. g. StarCraft and poker.
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow.
Auto-view contrastive learning for few-shot image recognition
Few-shot learning aims to recognize new classes with few annotated instances within each category.
Query-Key Normalization for Transformers
Low-resource language translation is a challenging but socially valuable NLP task.
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
SynFuzz: Efficient Concolic Execution via Branch Condition Synthesis
Then it uses oracle-guided program synthesis to reconstruct the symbolic expression based on input-output pairs.
Cryptography and Security
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
