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Hard Attention

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TitleStatusHype
Comparison of different Unique hard attention transformer models by the formal languages they can recognize—0
Characterizing the Expressivity of Transformer Language Models—0
Exact Expressive Power of Transformers with Padding—0
Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision—0
NoPE: The Counting Power of Transformers with No Positional Encodings—0
Neuroevolution of Self-Attention Over Proto-Objects—0
AttentionDrop: A Novel Regularization Method for Transformer Models—0
Center-guided Classifier for Semantic Segmentation of Remote Sensing ImagesCode0
Unique Hard Attention: A Tale of Two Sides—0
Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers—0
Ehrenfeucht-Haussler Rank and Chain of Thought—0
Simulating Hard Attention Using Soft Attention—0
Transformers in Uniform TC^0—0
Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal—0
Hard-Attention Gates with Gradient Routing for Endoscopic Image ComputingCode1
TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based VisionCode0
Transformers as Transducers—0
Recurrent Alignment with Hard Attention for Hierarchical Text RatingCode0
GQHAN: A Grover-inspired Quantum Hard Attention Network—0
Mutual Distillation Learning For Person Re-IdentificationCode1
Multi-View Unsupervised Image Generation with Cross Attention Guidance—0
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters—0
Vamos: Versatile Action Models for Video UnderstandingCode0
Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages—0
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning—0
Logical Languages Accepted by Transformer Encoders with Hard Attention—0
Investigation of Architectures and Receptive Fields for Appearance-based Gaze EstimationCode1
Average-Hard Attention Transformers are Constant-Depth Uniform Threshold Circuits—0
On the Learning Dynamics of Attention NetworksCode0
HAT-CL: A Hard-Attention-to-the-Task PyTorch Library for Continual LearningCode0
Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion DiagnosisCode1
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan—0
Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images—0
Learning to Perceive in Deep Model-Free Reinforcement LearningCode0
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays—0
Dual Attention Networks for Few-Shot Fine-Grained RecognitionCode0
Table Retrieval May Not Necessitate Table-specific Model DesignCode1
Binding Actions to Objects in World ModelsCode0
Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity—0
Consistency driven Sequential Transformers Attention Model for Partially Observable ScenesCode0
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model—0
You Only Need One Model for Open-domain Question Answering—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
A Probabilistic Hard Attention Model For Sequentially Observed ScenesCode0
Understanding Interlocking Dynamics of Cooperative RationalizationCode0
Sharp Attention for Sequence to Sequence Learning—0
Specialized Transformers: Faster, Smaller and more Accurate NLP Models—0
Saturated Transformers are Constant-Depth Threshold Circuits—0
Multimodal Emergent Fake News Detection via Meta Neural Process Networks—0
Self-Attention Networks Can Process Bounded Hierarchical LanguagesCode1
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