10 citations · 11 across the 6 of their papers we have counts for
7 papers
Analyzing Similarity Metrics for Data Selection for Language Model Pretraining
Dylan Sam, Ayan Chakrabarti, Afshin Rostamizadeh +3
Measuring similarity between training examples is critical for curating high-quality and diverse pretraining datasets for language models. However, similarity is typically computed…
LatentCRF: Continuous CRF for Efficient Latent Diffusion
Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit +5
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability.…
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs
Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh +12
A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such…
SoftSRV: Learn to Generate Targeted Synthetic Data
Giulia DeSalvo, Jean-Fracois Kagy, Lazaros Karydas +2
We present a novel framework, SoftSRV, that is used to generate targeted synthetic fine-tuning data for improving task-specific model performance. Given a sample from a target dist…
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit +3
As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID).…
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection
Ke Ye, Heinrich Jiang, Afshin Rostamizadeh +6
Pre-training large language models is known to be extremely resource intensive and often times inefficient, under-utilizing the information encapsulated in the training text sequen…
A Weighted K-Center Algorithm for Data Subset Selection
Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar
The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental prob…