Search Results for author:
Found 16 papers, 6 papers with code
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning
In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency.
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
We investigate the combination of encoder-decoder LLMs with both encoder-decoder and decoder-only SLMs from different model families and only require fine-tuning of the SLM.
MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content.
Revisiting Single-gated Mixtures of Experts
Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time.
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images.
SALISA: Saliency-based Input Sampling for Efficient Video Object Detection
In this paper, we propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection that allows for heavy down-sampling of unimportant background regions while preserving the fine-grained details of a high-resolution image.
FrameExit: Conditional Early Exiting for Efficient Video Recognition
In this paper, we propose a conditional early exiting framework for efficient video recognition.
Skip-Convolutions for Efficient Video Processing
We reformulate standard convolution to be efficiently computed on residual frames: each layer is coupled with a binary gate deciding whether a residual is important to the model prediction,~\eg foreground regions, or it can be safely skipped, e. g. background regions.
TimeGate: Conditional Gating of Segments in Long-range Activities
When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes.
Conditional Channel Gated Networks for Task-Aware Continual Learning
Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available.
Ranked #3 on
Continual Learning
on ImageNet-50 (5 tasks)
Batch-Shaping for Learning Conditional Channel Gated Networks
To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
The importance of stain normalization in colorectal tissue classification with convolutional networks
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture.
