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Found 18 papers, 8 papers with code
Combating Bilateral Edge Noise for Robust Link Prediction
To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
IBoxCLA: Towards Robust Box-supervised Segmentation of Polyp via Improved Box-dice and Contrastive Latent-anchors
In response, we propose two innovative learning fashions, Improved Box-dice (IBox) and Contrastive Latent-Anchors (CLA), and combine them to train a robust box-supervised model IBoxCLA.
Developing and Refining a Multifunctional Facial Recognition System for Older Adults with Cognitive Impairments: A Journey Towards Enhanced Quality of Life
In an era where the global population is aging significantly, cognitive impairments among the elderly have become a major health concern.
LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking
As an example, our fastest variant, LiteTrack-B4, achieves 65. 2% AO on the GOT-10k benchmark, surpassing all preceding efficient trackers, while running over 100 fps with ONNX on the Jetson Orin NX edge device.
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection
The Internet and social media have altered how individuals access news in the age of instantaneous information distribution.
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Due to the existence of similarity trap, such consistency regularization improperly enhances the consistency possiblity of the model's predictions between segment pairs, and thus reduces the confidence in reward learning, since the augmented distribution does not match with the original one in PbRL.
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards.
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors.
Exploring Model Dynamics for Accumulative Poisoning Discovery
In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information.
Beyond Reward: Offline Preference-guided Policy Optimization
Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively.
Offline Experience Replay for Continual Offline Reinforcement Learning
In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks.
Perspective Phase Angle Model for Polarimetric 3D Reconstruction
Current polarimetric 3D reconstruction methods, including those in the well-established shape from polarization literature, are all developed under the orthographic projection assumption.
Condition-Invariant and Compact Visual Place Description by Convolutional Autoencoder
We employ a high-level layer of a pre-trained CNN to generate features, and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time.
Off-policy Learning for Multiple Loggers
To make full use of such historical data, learning policies from multiple loggers becomes necessary.
Differential Equations for Modeling Asynchronous Algorithms
Then we conduct theoretical analysis on the convergence rates of ASGD algorithm based on the continuous approximation.
Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning
From the advertisers' side, participating in ranking the search results by paying for the sponsored search advertisement to attract more awareness and purchase facilitates their commercial goal.
Image color transfer to evoke different emotions based on color combinations
In this paper, a color transfer framework to evoke different emotions for images based on color combinations is proposed.
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution
This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators.
