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Found 57 papers, 13 papers with code
Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated Accuracy
This study addresses the problem of calibrating network confidence while adapting a model that was originally trained on a source domain to a target domain using unlabeled samples from the target domain.
A conformalized learning of a prediction set with applications to medical imaging classification
Medical imaging classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, which prevents their deployment in medical clinics.
The Power of Summary-Source Alignments
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.
A Conformal Prediction Score that is Robust to Label Noise
We introduce a conformal score that is robust to label noise.
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels.
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks.
Conformal Nucleus Sampling
Language models generate text based on successively sampling the next word.
Proposition-Level Clustering for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Long Context Question Answering via Supervised Contrastive Learning
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents.
Network calibration by weight scaling
We derive an optimization method that is based on a closed form solution for the optimal weight scaling in each bin of a discretized value of the prediction confidence.
Denoising Word Embeddings by Averaging in a Shared Space
We introduce a new approach for smoothing and improving the quality of word embeddings.
Speech enhancement with mixture-of-deep-experts with clean clustering pre-training
The experts estimate a mask from the noisy input and the final mask is then obtained as a weighted average of the experts' estimates, with the weights determined by the gating DNN.
A Mixture of Variational Autoencoders for Deep Clustering
In this study we propose a deep clustering algorithm that utilizes variational auto encoder (VAE) framework with a multi encoder-decoder neural architecture.
Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are an integral part of communication applications and boost the reliability of transmission.
Dynamically locating multiple speakers based on the time-frequency domain
In this study we present a deep neural network-based online multi-speaker localisation algorithm based on a multi-microphone array.
The Role of Regularization in Shaping Weight and Node Pruning Dependency and Dynamics
The pressing need to reduce the capacity of deep neural networks has stimulated the development of network dilution methods and their analysis.
A Locally Linear Procedure for Word Translation
We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices.
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic tasks.
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.
FCN Approach for Dynamically Locating Multiple Speakers
In this paper, we present a deep neural network-based online multi-speaker localisation algorithm.
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are an integral part of communication applications, boosting the reliability of transmission.
A Multi-Pairwise Extension of Procrustes Analysis for Multilingual Word Translation
In this paper we present a novel approach to simultaneously representing multiple languages in a common space.
Multilingual word translation using auxiliary languages
For each source word, we first search for the most relevant auxiliary languages.
A Soft STAPLE Algorithm Combined with Anatomical Knowledge
Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings.
Aligning Vector-spaces with Noisy Supervised Lexicon
The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces.
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net
We show that the time consuming local annotations involved in supervised learning can be addressed by a weakly supervised method that can leverage a subset of locally annotated data.
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net
The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging.
Precise Detection in Densely Packed Scenes
We propose a novel, deep-learning based method for precise object detection, designed for such challenging settings.
Ranked #4 on
Dense Object Detection
on SKU-110K
Aligning Vector-spaces with Noisy Supervised Lexicons
The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces.
Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation
Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training.
Deep Clustering Based on a Mixture of Autoencoders
The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network.
Self-Normalization Properties of Language Modeling
Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function.
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Then we present a novel scheme for liver lesion classification using CNN.
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs).
Anatomical Data Augmentation For CNN based Pixel-wise Classification
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network.
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for researchers.
A Simple Language Model based on PMI Matrix Approximations
Specifically, we show that with minor modifications to word2vec's algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models.
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function
In this paper we define a measure of dependency between two random variables, based on the Jensen-Shannon (JS) divergence between their joint distribution and the product of their marginal distributions.
Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding
This paper presents a novel deep learning architecture to classify structured objects in datasets with a large number of visually similar categories.
Domain Adaptation For Formant Estimation Using Deep Learning
We then freeze the parameters of the trained network and use several different datasets to train an adaptation layer that makes the obtained network universal in the sense that it works well for a variety of speakers and speech domains with very different characteristics.
PMI Matrix Approximations with Applications to Neural Language Modeling
The obtained language modeling is closely related to NCE language models but is based on a simplified objective function.
A Semisupervised Approach for Language Identification based on Ladder Networks
In this study we address the problem of training a neuralnetwork for language identification using both labeled and unlabeled speech samples in the form of i-vectors.
A Gaussian Tree Approximation for Integer Least-Squares
The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph.
ICA based on a Smooth Estimation of the Differential Entropy
In this paper we introduce the MeanNN approach for estimation of main information theoretic measures such as differential entropy, mutual information and divergence.
