Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Arefin, Md Rifat"'
Autor:
Arefin, Md Rifat, Subbaraj, Gopeshh, Gontier, Nicolas, LeCun, Yann, Rish, Irina, Shwartz-Ziv, Ravid, Pal, Christopher
Decoder-only Transformers often struggle with complex reasoning tasks, particularly arithmetic reasoning requiring multiple sequential operations. In this work, we identify representation collapse in the model's intermediate layers as a key factor li
Externí odkaz:
http://arxiv.org/abs/2411.02344
Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception may achieve higher out-of-distribution generalization. In this study, we
Externí odkaz:
http://arxiv.org/abs/2409.05817
Autor:
Arefin, Md Rifat, Zhang, Yan, Baratin, Aristide, Locatello, Francesco, Rish, Irina, Liu, Dianbo, Kawaguchi, Kenji
Publikováno v:
ICLM 2024
Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlat
Externí odkaz:
http://arxiv.org/abs/2402.13368
Autor:
Darvishi-Bayazi, Mohammad-Javad, Ghaemi, Mohammad Sajjad, Lesort, Timothee, Arefin, Md Rifat, Faubert, Jocelyn, Rish, Irina
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate
Externí odkaz:
http://arxiv.org/abs/2309.10910
Autor:
Lesort, Timothée, Ostapenko, Oleksiy, Misra, Diganta, Arefin, Md Rifat, Rodríguez, Pau, Charlin, Laurent, Rish, Irina
Building learning agents that can progressively learn and accumulate knowledge is the core goal of the continual learning (CL) research field. Unfortunately, training a model on new data usually compromises the performance on past data. In the CL lit
Externí odkaz:
http://arxiv.org/abs/2207.04543
Autor:
Ostapenko, Oleksiy, Lesort, Timothee, Rodríguez, Pau, Arefin, Md Rifat, Douillard, Arthur, Rish, Irina, Charlin, Laurent
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foun
Externí odkaz:
http://arxiv.org/abs/2205.00329
Autor:
Hu, Shell Xu, Arefin, Md Rifat, Nguyen, Viet-Nhat, Dipani, Alish, Pitkow, Xaq, Tolias, Andreas Savas
To extract the voice of a target speaker when mixed with a variety of other sounds, such as white and ambient noises or the voices of interfering speakers, we extend the Transformer network to attend the most relevant information with respect to the
Externí odkaz:
http://arxiv.org/abs/2105.00609
Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites. An effective and efficient recommender system benefits both the seller and the buyer significantly. W
Externí odkaz:
http://arxiv.org/abs/2012.00501
Autor:
Darvishi-Bayazi, Mohammad-Javad, Ghaemi, Mohammad Sajjad, Lesort, Timothee, Arefin, Md. Rifat, Faubert, Jocelyn, Rish, Irina
Publikováno v:
In Computers in Biology and Medicine February 2024 169
Autor:
Deudon, Michel, Kalaitzis, Alfredo, Goytom, Israel, Arefin, Md Rifat, Lin, Zhichao, Sankaran, Kris, Michalski, Vincent, Kahou, Samira E., Cornebise, Julien, Bengio, Yoshua
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to th
Externí odkaz:
http://arxiv.org/abs/2002.06460