123 citations · 212 across the 13 of their papers we have counts for
13 papers
Squeezed Diffusion Models
Jyotirmai Singh, Samar Khanna, James Burgess
Diffusion models typically inject isotropic Gaussian noise, disregarding structure in the data. Motivated by the way quantum squeezed states redistribute uncertainty according to t…
Mercury: Ultra-Fast Language Models Based on Diffusion
Inception Labs, Samar Khanna, Siddhant Kharbanda +10
We present Mercury, a new generation of commercial-scale large language models (LLMs) based on diffusion. These models are parameterized via the Transformer architecture and traine…
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen +5
Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they…
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts
Samar Khanna, Medhanie Irgau, David B. Lobell +1
Parameter-efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA) can effectively adapt large pre-trained foundation models to downstream tasks using only a smal…
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception
Louis Foucard, Samar Khanna, Yi Shi +4
In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/ima…
Large Language Models are Geographically Biased
Rohin Manvi, Samar Khanna, Marshall Burke +2
Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation…
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Samar Khanna, Patrick Liu, Linqi Zhou +5
Diffusion models have achieved state-of-the-art results on many modalities including images, speech, and video. However, existing models are not tailored to support remote sensing…
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
Rohin Manvi, Samar Khanna, Gengchen Mai +3
The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can…
Denoising Diffusion Bridge Models
Linqi Zhou, Aaron Lou, Samar Khanna +1
Diffusion models are powerful generative models that map noise to data using stochastic processes. However, for many applications such as image editing, the model input comes from…
Differentiable Weight Masks for Domain Transfer
Samar Khanna, Skanda Vaidyanath, Akash Velu
One of the major drawbacks of deep learning models for computer vision has been their inability to retain multiple sources of information in a modular fashion. For instance, given…
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
Rylan Schaeffer, Kateryna Pistunova, Samar Khanna +2
Language models can be prompted to reason through problems in a manner that significantly improves performance. However, \textit{why} such prompting improves performance is unclear…
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Yezhen Cong, Samar Khanna, Chenlin Meng +6
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery pres…
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
Samar Khanna, Bram Wallace, Kavita Bala +1
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium re…