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Found 30 papers, 13 papers with code
Efficient Post-Training Refinement of Latent Reasoning in Large Language Models
Reasoning is a key component of language understanding in Large Language Models.
Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback
Foundation models showed promise in this area, but existing approaches suffer from: 1) They are pretrained on general-purpose data distributions, making them less effective for domain-specific tasks; and 2) their training objectives focus on token-level alignment, overlooking mathematical semantics, which can lead to inaccurate equations.
Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation
Feature Transformation (FT) crafts new features from original ones via mathematical operations to enhance dataset expressiveness for downstream models.
Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories
As a widely-used and practical tool, feature engineering transforms raw data into discriminative features to advance AI model performance.
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement.
Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content
The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye.
Deep Active Learning in the Open World
Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations.
Topology-aware Reinforcement Feature Space Reconstruction for Graph Data
Feature space is an environment where data points are vectorized to represent the original dataset.
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches
In this thesis, we study ways toward robust OoD generalization for deep learning, i. e., its performance is not susceptible to distribution shift in the test data.
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
This paper introduces a novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild.
Out-of-Distribution Learning with Human Feedback
Out-of-distribution (OOD) learning often relies heavily on statistical approaches or predefined assumptions about OOD data distributions, hindering their efficacy in addressing multifaceted challenges of OOD generalization and OOD detection in real-world deployment environments.
It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Specifically, we first use GNN to learn the representations of users and items in different modalities and split each modal representation into generic and unique parts.
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
To alleviate popularity bias, existing efforts focus on emphasizing unpopular items or separating the correlation between item representations and their popularity.
Double Correction Framework for Denoising Recommendation
To tackle the above limitations, we propose a Double Correction Framework for Denoising Recommendation (DCF), which contains two correction components from views of more precise sample dropping and avoiding more sparse data.
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
How to quickly provide recommendations for new items at the inference time is challenging.
HYPO: Hyperspherical Out-of-Distribution Generalization
Out-of-distribution (OOD) generalization is critical for machine learning models deployed in the real world.
Image change detection with only a few samples
This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available.
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively.
Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models
In this paper, we employ interpolated generative models to generate OoD samples at training time via data augmentation.
Voxel Transformer for 3D Object Detection
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.
Ranked #3 on
3D Object Detection
on waymo vehicle
(L1 mAP metric)
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.
Ranked #2 on
3D Object Detection
on waymo vehicle
(AP metric)
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
In this work, we propose robust Neural Architecture Search for OoD generalization (NAS-OoD), which optimizes the architecture with respect to its performance on generated OoD data by gradient descent.
Ranked #1 on
Domain Generalization
on NICO Vehicle
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification
The classification branch extracts global group priors by learning correlations among image clusters.
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting
Noting the scarcity and low quality (in terms of resolution and scene diversity) of the publicly available video crowd datasets, we have collected and built a large-scale video crowd counting datasets, VidCrowd, to contribute to the community.
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal
Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc.
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization.
Crowd Counting on Images with Scale Variation and Isolated Clusters
Designing a general crowd counting algorithm applicable to a wide range of crowd images is challenging, mainly due to the possibly large variation in object scales and the presence of many isolated small clusters.
