Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Itzik Malkiel"'
Autor:
Noam Koenigstein, Jonathan Weill, Ori Katz, Itzik Malkiel, Idan Rejwan, Avi Caciularu, Oren Barkan
Publikováno v:
CIKM
We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6ad07e3e83174dd148cfa6f08f039bf
We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task. We focus on downstream tasks of learning similarities for recommenda
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a045c69568b3c9c45d7e6ce955151bd
http://arxiv.org/abs/2208.06610
http://arxiv.org/abs/2208.06610
Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. Despite significant progress in the field, the explanations for similari
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d79e8a18d044aa5685a11e9ffd86ba4b
http://arxiv.org/abs/2208.06612
http://arxiv.org/abs/2208.06612
Autor:
Noam Koenigstein, Avi Caciularu, Edan Hauon, Ori Katz, Itzik Malkiel, Oren Barkan, Omri Armstrong
Publikováno v:
CIKM
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has become a major area of interest for the NLP community. In this work, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b93c33f4e45b18775d47c94e1183febb
http://arxiv.org/abs/2204.11073
http://arxiv.org/abs/2204.11073
Publikováno v:
AAAI
Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations – a process in which each word in sentence A attends to all words in sentence B and
Publikováno v:
MRS Bulletin. 45:221-229
The past decade has witnessed the advent of nanophotonics, where light–matter interaction is shaped, almost at will, with human-made designed nanostructures. However, the design process for these nanostructures has remained complex, often relying o
Publikováno v:
ICCP
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f4e77b21a21ce5865a0fa39cd347e84
http://arxiv.org/abs/2104.01889
http://arxiv.org/abs/2104.01889
Autor:
Itzik Malkiel, Lior Wolf
Publikováno v:
EACL
Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. We present a method that leverages the second phase to its fullest, by applying an extensive n
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd9bf1a4df77a0e3cbac1ea0ee3e0998
Autor:
Itzik Malkiel, Avi Caciularu, Oren Barkan, Omri Armstrong, Ori Katz, Amir Hertz, Noam Koenigstein
Publikováno v:
CIKM
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccae6b21b776d8b21fe99970892a4b85