Search Results - "Ermis, Beyza"

Link prediction in heterogeneous data via generalized coupled tensor factorization by Ermiş, Beyza, Acar, Evrim, Cemgil, A. Taylan

“…This study deals with missing link prediction, the problem of predicting the existence of missing connections between entities of interest. We approach the…”
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Exact MCMC with differentially private moves: Revisiting the penalty algorithm in a data privacy framework by Yıldırım, Sinan, Ermiş, Beyza

“…We view the penalty algorithm of Ceperley and Dewing (J Chem Phys 110(20):9812–9820, 1999 ), a Markov chain Monte Carlo algorithm for Bayesian inference, in…”
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A Multicenter Randomized Controlled Trial of Microbiome-Based Artificial Intelligence-Assisted Personalized Diet vs Low-Fermentable Oligosaccharides, Disaccharides, Monosaccharides, and Polyols Diet: A Novel Approach for the Management of Irritable Bowel Syndrome by Tunali, Varol, Arslan, Naciye Çiğdem, Ermiş, Beyza Hilal, Derviş Hakim, Gözde, Gündoğdu, Aycan, Hora, Mehmet, Nalbantoğlu, Özkan Ufuk

“…Personalized management strategies are pivotal in addressing irritable bowel syndrome (IBS). This multicenter randomized controlled trial focuses on comparing…”
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Mo1870 A MULTICENTER RANDOMIZED CONTROLLED TRIAL OF MICROBIOMEBASED ARTIFICIAL INTELLIGENCE-ASSISTED PERSONALIZED DIET VS FODMAP DIET: A NOVEL PERSONALIZED THERAPEUTIC APPROACH FOR IBS by TUNALI, Varol, ARSLAN, Naciye C., HAKIM, Gözde DERVIS, GUNDOGDU, Aycan, ERMIS, Beyza Hilal, NALBANTOGLU, Ozkan Ufuk

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Learning mixed divergences in coupled matrix and tensor factorization models by Simsekli, Umut, Cemgil, Ali Taylan, Ermis, Beyza

“…Coupled tensor factorization methods are useful for sensor fusion, combining information from several related datasets by simultaneously approximating them by…”
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Response to Dahly and Morris by Tunali, Varol, Ermiş, Beyza Hilal, Nalbantoğlu, Özkan Ufuk

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Exact MCMC with differentially private moves by Yıldırım, Sinan, Ermiş, Beyza

“…We view the penalty algorithm of Ceperley and Dewing (J Chem Phys 110(20):9812–9820, 1999), a Markov chain Monte Carlo algorithm for Bayesian inference, in the…”
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Continual Learning with Transformers for Image Classification by Ermis, Beyza, Zappella, Giovanni, Wistuba, Martin, Rawal, Aditya, Archambeau, Cedric

“…In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn…”
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Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress by Odumakinde, Ayomide, D'souza, Daniel, Verga, Pat, Ermis, Beyza, Hooker, Sara

“…The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate…”
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From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models by Pozzobon, Luiza, Lewis, Patrick, Hooker, Sara, Ermis, Beyza

“…To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual…”
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Elo Uncovered: Robustness and Best Practices in Language Model Evaluation by Boubdir, Meriem, Kim, Edward, Ermis, Beyza, Hooker, Sara, Fadaee, Marzieh

“…In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used…”
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Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation by Boubdir, Meriem, Kim, Edward, Ermis, Beyza, Fadaee, Marzieh, Hooker, Sara

“…Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately…”
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Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models by Pozzobon, Luiza, Ermis, Beyza, Lewis, Patrick, Hooker, Sara

“…Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of…”
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On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research by Pozzobon, Luiza, Ermis, Beyza, Lewis, Patrick, Hooker, Sara

“…Perception of toxicity evolves over time and often differs between geographies and cultural backgrounds. Similarly, black-box commercially available APIs for…”
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Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning by Aakanksha, Ahmadian, Arash, Goldfarb-Tarrant, Seraphina, Ermis, Beyza, Fadaee, Marzieh, Hooker, Sara

“…Large Language Models (LLMs) have been adopted and deployed worldwide for a broad variety of applications. However, ensuring their safe use remains a…”
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Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? by Öncel, Fırat, Bethge, Matthias, Ermis, Beyza, Ravanelli, Mirco, Subakan, Cem, Yıldız, Çağatay

“…In the last decade, the generalization and adaptation abilities of deep learning models were typically evaluated on fixed training and test distributions…”
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The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm by Aakanksha, Ahmadian, Arash, Ermis, Beyza, Goldfarb-Tarrant, Seraphina, Kreutzer, Julia, Fadaee, Marzieh, Hooker, Sara

“…A key concern with the concept of "alignment" is the implicit question of "alignment to what?". AI systems are increasingly used across the world, yet safety…”
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Continual Learning with Transformers for Image Classification by Ermis, Beyza, Zappella, Giovanni, Wistuba, Martin, Rawal, Aditya, Archambeau, Cedric

“…In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn…”
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Investigating Continual Pretraining in Large Language Models: Insights and Implications by Yıldız, Çağatay, Ravichandran, Nishaanth Kanna, Punia, Prishruit, Bethge, Matthias, Ermis, Beyza

“…This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and…”
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Memory Efficient Continual Learning with Transformers by Ermis, Beyza, Zappella, Giovanni, Wistuba, Martin, Rawal, Aditya, Archambeau, Cedric

“…In many real-world scenarios, data to train machine learning models becomes available over time. Unfortunately, these models struggle to continually learn new…”
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