Search Results for author:
Found 12 papers, 4 papers with code
CountPath: Automating Fragment Counting in Digital Pathology
Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards.
One Model to Train them All: Hierarchical Self-Distillation for Enhanced Early Layer Embeddings
MODULARSTARENCODER is trained with a novel self-distillation mechanism that significantly improves lower-layer representations-allowing different portions of the model to be used while still maintaining a good trade-off in terms of performance.
PairBench: A Systematic Framework for Selecting Reliable Judge VLMs
As large vision language models (VLMs) are increasingly used as automated evaluators, understanding their ability to effectively compare data pairs as instructed in the prompt becomes essential.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #53 on
Code Generation
on MBPP
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
On the internal dataset, the method shows an accuracy of 93. 44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0. 996.
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples.
Ranked #1 on
Colorectal Polyps Characterization
on CRC
Generalizing to unseen domains via distribution matching
In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).
Ranked #70 on
Domain Generalization
on PACS
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment
Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.
