Search Results for author:
Found 23 papers, 4 papers with code
Learning and Evaluating a Differentially Private Pre-trained Language Model
One method to guarantee the privacy of such individuals is to train a differentially-private language model, but this usually comes at the expense of model performance.
Dreamix: Video Diffusion Models are General Video Editors
Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #1 on
Question Answering
on MedQA-USMLE
Fast Inference from Transformers via Speculative Decoding
Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model.
UniTune: Text-Driven Image Editing by Fine Tuning an Image Generation Model on a Single Image
We present UniTune, a simple and novel method for general text-driven image editing.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
Discovering novel systemic biomarkers in photos of the external eye
On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7. 3-13. 2%.
TRUE: Re-evaluating Factual Consistency Evaluation
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.
Flood forecasting with machine learning models in an operational framework
During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287, 000 km2, home to more than 350M people.
Adversarial Robustness of Streaming Algorithms through Importance Sampling
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions.
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm.
Adversarially Robust Streaming Algorithms via Differential Privacy
A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary.
Detecting Deficient Coverage in Colonoscopies
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
Spectral Algorithm for Low-rank Multitask Regression
In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model.
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
In this paper, we present and evaluate finetuning techniques to improve ASR for users with non-standard speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Audio De-identification - a New Entity Recognition Task
To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
LSH Microbatches for Stochastic Gradients: Value in Rearrangement
We make a principled argument for the properties of our arrangements that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal distribution of training examples.
Audio De-identification: A New Entity Recognition Task
To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Differentially Private Learning of Geometric Concepts
We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex).
ML for Flood Forecasting at Scale
We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.
Self-Similar Epochs: Value in Arrangement
Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples.
