19 accepted papers
Introducing a domain shift, such as a change in language or environment, to a well-trained speech enhancement system can cause severe performance degradation. Most current research assumes that a domain shift has already been detected and focuses on either supervised or unsupervised domain adaptatio…
"Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. It has attracted growing attention in recent years, where existing approaches focus on self-training that usually includes pseudo-labeling techniques. In this paper, w…
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.In this context, alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training…
Language models generate text based on successively sampling the next word. A decoding procedure based on nucleus (top-p) sampling chooses from the smallest possible set of words whose cumulative probability exceeds the probability p. In this work, we assess whether a top-p set is indeed aligned wit…
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-tr…
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the question. In this work, we propose a novel me…
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, clusters were leveraged to indicate information saliency as well as to avoid redundancy. Such prior methods focused on cluster…
Batch normalization (BN) is a key component of most neural network architectures. A major weakness of Batch Normalization is its critical dependence on having a reasonably large batch size, due to the inherent approximation of estimating the mean and variance with a single batch of data. Another wea…
In this study we present a mixture of deep experts (MoDE) neural-network architecture for single microphone speech enhancement. Our architecture comprises a set of deep neural networks (DNNs), each of which is an ‘expert’ in a different speech spectral pattern such as phoneme. A gating DNN is respon…
In speech enhancement, the use of supervised algorithms in the form of deep neural networks (DNNs) has become tremendously popular in recent years. The target function of the DNN (and the associated estimators) is often either a masking function applied to the noisy spectrum, or the clean log-spectr…
Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solu…
In this study we propose a deep clustering algorithm that extends the k-means algorithm. Each cluster is represented by an autoencoder instead of a single centroid vector. Each data point is associated with the autoencoder which yields the minimal reconstruction error. The optimal clustering is foun…
The information-bottleneck (IB) principle is defined in terms of mutual information. This study defines mutual information between two random variables using the Jensen-Shannon (JS) divergence instead of the standard definition which is based on the Kullback-Leibler (KL) divergence. We reformulate t…
We address the problem of adding new classes to an existing classifier without hurting the original classes, when no access is allowed to any sample from the original classes. This problem arises frequently since models are often shared without their training data, due to privacy and data ownership…
Man-made scenes are often densely packed, containing numerous objects, often identical, positioned in close proximity. We show that precise object detection in such scenes remains a challenging frontier even for state-of-the-art object detectors. We propose a novel, deep-learning based method for pr…
In this paper, we present a new control mechanism for LCMV beamforming. Application of the LCMV beamformer to speaker separation tasks requires accurate estimates of its building blocks, e.g. the noise spatial cross-power spectral density (cPSD) matrix and the relative transfer function (RTF) of all…
The availability of large datsets has enabled neural networks to achieve impressive recognition results. However, the presence of inaccurate class labels is known to deteriorate the performance of even the best classifiers in a broad range of classification problems. Noisy labels also tend to be m…
In this study we address the problem of integrating information from several experts with unknown levels of expertise. In the usual setup each expert expresses her opinion by choosing one of the options. Here we assume that each expert provides her opinion in a soft manner via a distribution on the…
In this study we address the problem of training a neural network based on data with unreliable labels. We introduce an extra noise layer by assuming that the observed labels were created from the true labels by passing through a noisy channel whose parameters are unknown. We propose a method that s…