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TitleStatusHype
Flow-matching -- efficient coarse-graining of molecular dynamics without forcesCode1
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of RedundancyCode1
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly DetectionCode1
Backbone is All Your Need: A Simplified Architecture for Visual Object TrackingCode1
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and ClassificationCode1
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLCode1
Not All Patches are What You Need: Expediting Vision Transformers via Token ReorganizationsCode1
pNLP-Mixer: an Efficient all-MLP Architecture for LanguageCode1
It's All in the Head: Representation Knowledge Distillation through Classifier SharingCode1
Cost Aggregation Is All You Need for Few-Shot SegmentationCode1
Danna-Sep: Unite to separate them allCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial RobustnessCode1
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather ConditionsCode1
CDNet is all you need: Cascade DCN based underwater object detection RCNNCode1
Perceiving and Modeling Density is All You Need for Image DehazingCode1
Gradients are Not All You NeedCode1
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGANCode1
Hyperparameter Tuning is All You Need for LISTACode1
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?Code1
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR dataCode1
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low LatencyCode1
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning ObjectiveCode1
Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer visionCode1
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