Zobrazeno 1 - 10
of 1 447
pro vyhledávání: '"Farnia, P."'
Autor:
Ospanov, Azim, Zhang, Jingwei, Jalali, Mohammad, Cao, Xuenan, Bogdanov, Andrej, Farnia, Farzan
While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference datasets. Recently, the reference-
Externí odkaz:
http://arxiv.org/abs/2407.02961
Existing frameworks for evaluating and comparing generative models typically target an offline setting, where the evaluator has access to full batches of data produced by the models. However, in many practical scenarios, the goal is to identify the b
Externí odkaz:
http://arxiv.org/abs/2406.07451
Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weight perturbations are not considered. However, the widely used large lan
Externí odkaz:
http://arxiv.org/abs/2406.07017
The Langevin Dynamics framework, which aims to generate samples from the score function of a probability distribution, is widely used for analyzing and interpreting score-based generative modeling. While the convergence behavior of Langevin Dynamics
Externí odkaz:
http://arxiv.org/abs/2406.02017
Autor:
Zhang, Jingwei, Farnia, Farzan
Transportation of samples across different domains is a central task in several machine learning problems. A sensible requirement for domain transfer tasks in computer vision and language domains is the sparsity of the transportation map, i.e., the t
Externí odkaz:
http://arxiv.org/abs/2405.07489
An interpretable comparison of generative models requires the identification of sample types produced more frequently by each of the involved models. While several quantitative scores have been proposed in the literature to rank different generative
Externí odkaz:
http://arxiv.org/abs/2405.02700
While adversarial training methods have resulted in significant improvements in the deep neural nets' robustness against norm-bounded adversarial perturbations, their generalization performance from training samples to test data has been shown to be
Externí odkaz:
http://arxiv.org/abs/2404.08980
Autor:
Fallah, Pouya, Gooran, Soroush, Jafarinasab, Mohammad, Sadeghi, Pouya, Farnia, Reza, Tarabkhah, Amirreza, Taghavi, Zainab Sadat, Sameti, Hossein
Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores methods for detecting hallucinations in three SemEval-2024 Task 6 tasks:
Externí odkaz:
http://arxiv.org/abs/2404.04845
Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the simple gradient and integrated gradient algorithms, often lack desired
Externí odkaz:
http://arxiv.org/abs/2404.04647
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible p
Externí odkaz:
http://arxiv.org/abs/2403.15434