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Found 44 papers, 9 papers with code
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.
Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pixel WSIs are only labeled at the slide level.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
The massive developments of generative model frameworks and architectures require principled methods for the evaluation of a model's novelty compared to a reference dataset or baseline generative models.
Genie: Generative Interactive Environments
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos.
Offline Actor-Critic Reinforcement Learning Scales to Large Models
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment.
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging.
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5. 07% to 5. 12% in Dice score and 4. 50% to 8. 48% in IOU.
DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks
Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs).
Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection
Data and model are the undoubtable two supporting pillars for LiDAR object detection.
A Generalist Dynamics Model for Control
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning
Compared to conventional full fine-tuning approaches, we fine-tune less than 1. 3% of the parameters, yet achieve a relative improvement of 1. 29%-13. 61% in accuracy and 3. 22%-27. 18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains
We conduct a set of experiments in the RGB-stacking environment, showing that planning with the learned skills and the associated model can enable zero-shot generalization to new tasks, and can further speed up training of policies via reinforcement learning.
MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope
Explaining the predictions of deep neural nets has been a topic of great interest in the computer vision literature.
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds
Attributed to the unstructured and sparse nature of point clouds, the transformer shows greater potential in point clouds data processing.
Local Learning on Transformers via Feature Reconstruction
In this paper, we propose a new mechanism for each local module, where instead of reconstructing the entire image, we reconstruct its input features, generated from previous modules.
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Our method outperforms previous dense matching methods by up to 7. 2% in average precision for detection and 5. 6% in average precision for instance segmentation tasks.
Generalizable Re-Identification from Videos with Cycle Association
In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos.
Domain Generalization
Generalizable Person Re-identification
+1
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.
Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates
We consider optimizing two-layer neural networks in the mean-field regime where the learning dynamics of network weights can be approximated by the evolution in the space of probability measures over the weight parameters associated with the neurons.
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention
This paper seeks to tackle the bin packing problem (BPP) through a learning perspective.
A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification
To tackle these problems, we propose a novel spatial and magnification based attention sampling strategy.
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory
With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i. e.}, deep image classification and deep metric learning (on long-tailed data).
Ranked #44 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning
In this paper we present an approach to learning policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow.
Multi-Modality Cascaded Fusion Technology for Autonomous Driving
Multi-modality fusion is the guarantee of the stability of autonomous driving systems.
Generalization Bounds for Convolutional Neural Networks
Convolutional neural networks (CNNs) have achieved breakthrough performances in a wide range of applications including image classification, semantic segmentation, and object detection.
Dual-module Inference for Efficient Recurrent Neural Networks
Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs.
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries.
An Optimal Transport View on Generalization
We derive upper bounds on the generalization error of learning algorithms based on their \emph{algorithmic transport cost}: the expected Wasserstein distance between the output hypothesis and the output hypothesis conditioned on an input example.
An Information-Theoretic View for Deep Learning
This upper bound shows that as the number of convolutional and pooling layers $L$ increases in the network, the expected generalization error will decrease exponentially to zero.
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
A video of our experimental results can be found at https://goo. gl/pWbpcF.
On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier
We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier.
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks.
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning.
Neural SLAM: Learning to Explore with External Memory
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments.
Reinforcement Learning (RL)
Simultaneous Localization and Mapping
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning.
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances.
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models.
