Search Results for author:
Found 16 papers, 11 papers with code
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
While several powerful distillation methods were recently proposed, the overall quality of student samples is typically lower compared to the teacher ones, which hinders their practical usage.
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters.
DeDrift: Robust Similarity Search under Content Drift
We investigate the impact of this "content drift" for large-scale similarity search tools, based on nearest neighbor search in embedding space.
Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
Finally, we show that our approach can adapt the pretrained model to the user-specified image and text description on the fly just for 4 seconds.
TabDDPM: Modelling Tabular Data with Diffusion Models
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities.
Petals: Collaborative Inference and Fine-tuning of Large Models
However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research that requires access to weights, attention or logits.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
Label-Efficient Semantic Segmentation with Diffusion Models
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance.
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
From a theoretical perspective, we rigorously analyze the time and space complexity of graph-based NNS, assuming that an n-element dataset is uniformly distributed within a d-dimensional ball of radius R in the hyperbolic space of curvature -1.
Distilling the Knowledge from Conditional Normalizing Flows
Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems.
Discovering Weight Initializers with Meta Learning
In this study, we propose a task-agnostic approach that discovers initializers for specific network architectures and optimizers by learning the initial weight distributions directly through the use of Meta-Learning.
Towards Similarity Graphs Constructed by Deep Reinforcement Learning
New algorithms for similarity graph construction are continuously being proposed and analyzed by both theoreticians and practitioners.
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.
Dimensionality Reduction
Multivariate Time Series Imputation
+3
GP-VAE: Deep Probabilistic Time Series Imputation
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.
Dimensionality Reduction
Multivariate Time Series Imputation
+2
Learning to Route in Similarity Graphs
Recently similarity graphs became the leading paradigm for efficient nearest neighbor search, outperforming traditional tree-based and LSH-based methods.
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
This work addresses the problem of billion-scale nearest neighbor search.
