Zobrazeno 1 - 10
of 36 072
pro vyhledávání: '"Mubarak ."'
Autor:
Beetham, James, Chakraborty, Souradip, Wang, Mengdi, Huang, Furong, Bedi, Amrit Singh, Shah, Mubarak
Many existing jailbreak techniques rely on solving discrete combinatorial optimization, while more recent approaches involve training LLMs to generate multiple adversarial prompts. However, both approaches require significant computational resources
Externí odkaz:
http://arxiv.org/abs/2412.05232
Autor:
Brook, Bindi S., Bulpett, Mathew, Curnow, Robin, Fraser, Emily, Hall, Eric J., Huang, Shiting, Mubarak, Mariam, Whitfield, Carl A.
This report relates to a study group hosted by the EPSRC funded network, Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME), and supported by SofTMech and Innovate UK, Business Connect. This report summarises the work unde
Externí odkaz:
http://arxiv.org/abs/2412.05141
Autor:
Croitoru, Florinel-Alin, Hiji, Andrei-Iulian, Hondru, Vlad, Ristea, Nicolae Catalin, Irofti, Paul, Popescu, Marius, Rusu, Cristian, Ionescu, Radu Tudor, Khan, Fahad Shahbaz, Shah, Mubarak
With the recent advancements in generative modeling, the realism of deepfake content has been increasing at a steady pace, even reaching the point where people often fail to detect manipulated media content online, thus being deceived into various ki
Externí odkaz:
http://arxiv.org/abs/2411.19537
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
This research aims to predict the price of rice in Banda Aceh after the occurrence of Covid-19. The last observation carried forward (LOCF) imputation technique has been used to solve the problem of missing values from this research data. Furthermore
Externí odkaz:
http://arxiv.org/abs/2411.15228
Autor:
Siddiqui, Nyle, Croitoru, Florinel Alin, Nayak, Gaurav Kumar, Ionescu, Radu Tudor, Shah, Mubarak
With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representations. While this generative data expansion may suffice for easier visual
Externí odkaz:
http://arxiv.org/abs/2411.07205
Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide geolocalization has been tackled before, but only using the image mod
Externí odkaz:
http://arxiv.org/abs/2411.06344
Autor:
Singh, Utsav, Chakraborty, Souradip, Suttle, Wesley A., Sadler, Brian M., Sahu, Anit Kumar, Shah, Mubarak, Namboodiri, Vinay P., Bedi, Amrit Singh
This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO le
Externí odkaz:
http://arxiv.org/abs/2411.00361
Diffusion models, widely used for image and video generation, face a significant limitation: the risk of memorizing and reproducing training data during inference, potentially generating unauthorized copyrighted content. While prior research has focu
Externí odkaz:
http://arxiv.org/abs/2410.21669
In this paper, we identify and leverage a novel `bright ending' (BE) anomaly in diffusion models prone to memorizing training images to address a new task: locating localized memorization regions within these models. BE refers to a distinct cross-att
Externí odkaz:
http://arxiv.org/abs/2410.21665