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Found 31 papers, 16 papers with code
Efficient Dataset Distillation via Minimax Diffusion
Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity, we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models.
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.
Model-enhanced Vector Index
We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.
Investigating Copyright Issues of Diffusion Models under Practical Scenarios
In our work, we tackle the limitations of previous studies by delving into partial copyright infringement, which treats parts of images as copyrighted content, using prompts that are considerably different from copyrighted topics.
Lightweight Super-Resolution Head for Human Pose Estimation
We first propose the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing.
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D medical image segmentation.
Subclass-balancing Contrastive Learning for Long-tailed Recognition
We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses.
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective
Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs.
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
An effective ranking model should give a personalized ranking list for each user according to the user preference.
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions.
Boosting Visual-Language Models by Exploiting Hard Samples
On a comprehensive zero-shot and retrieval benchmark, without training the model from scratch or utilizing additional data, HELIP consistently boosts existing models to achieve leading performance.
Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation
In this paper, we propose a novel module to explicitly extract motion and appearance information via a unifying operation.
Ranked #1 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(PSNR metric)
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass augmentation-free graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks.
BEBERT: Efficient and Robust Binary Ensemble BERT
Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks.
A Neural Corpus Indexer for Document Retrieval
To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model.
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.
Training Fair Deep Neural Networks by Balancing Influence
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application.
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.
Ranked #2 on
Medical Image Segmentation
on GlaS
(IoU metric)
Controllable Gradient Item Retrieval
A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.
Adversarial Data Generation of Multi-category Marked Temporal Point Processes with Sparse, Incomplete, and Small Training Samples
The transformation of training data to the distribution facilitates the accurate capture of underlying process characteristics despite the sparseness and incompleteness of data.
Secure Network Release with Link Privacy
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.
Secure Deep Graph Generation with Link Differential Privacy
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
Park: An Open Platform for Learning-Augmented Computer Systems
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.
Learning to Transfer via Modelling Multi-level Task Dependency
Besides, the understanding of relationships among tasks has been ignored by most of the current methods.
Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm
Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability.
