12 citations · 24 across the 11 of their papers we have counts for
11 papers
Learning from Reasoning Failures via Synthetic Data Generation
Gabriela Ben Melech Stan, Estelle Aflalo, Avinash Madasu +2
Training models on synthetic data has emerged as an increasingly important strategy for improving the performance of generative AI. This approach is particularly helpful for large…
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le +5
Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensurin…
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Raanan Y. Rohekar, Yaniv Gurwicz, Sungduk Yu +2
Are generative pre-trained transformer (GPT) models, trained only to predict the next token, implicitly learning a world model from which sequences are generated one token at a tim…
FastRM: An efficient and automatic explainability framework for multimodal generative models
Gabriela Ben-Melech Stan, Estelle Aflalo, Man Luo +5
Large Vision Language Models (LVLMs) have demonstrated remarkable reasoning capabilities over textual and visual inputs. However, these models remain prone to generating misinforma…
LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
Gabriela Ben Melech Stan, Estelle Aflalo, Raanan Yehezkel Rohekar +7
In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various…
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo +8
One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in…
LDM3D-VR: Latent Diffusion Model for 3D VR
Gabriela Ben Melech Stan, Diana Wofk, Estelle Aflalo +4
Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with…
LDM3D: Latent Diffusion Model for 3D
Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox +8
This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images f…
MuMUR : Multilingual Multimodal Universal Retrieval
Avinash Madasu, Estelle Aflalo, Gabriela Ben Melech Stan +4
Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is…
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Estelle Aflalo, Meng Du, Shao-Yen Tseng +4
Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability t…
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Shauharda Khadka, Estelle Aflalo, Mattias Marder +6
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is crit…