Search Results for author:
Found 24 papers, 9 papers with code
Designing Category-Level Attributes for Discriminative Visual Recognition
In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix.
$\propto$SVM for learning with label proportions
We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known.
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known.
Circulant Binary Embedding
To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix.
Video Event Detection by Inferring Temporal Instance Labels
To solve this problem, we propose a large-margin formulation which treats the instance labels as hidden latent variables, and simultaneously infers the instance labels as well as the instance-level classification model.
An exploration of parameter redundancy in deep networks with circulant projections
We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
Compact Nonlinear Maps and Circulant Extensions
This leads to much more compact maps without hurting the performance.
On Binary Embedding using Circulant Matrices
To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix.
Fast Orthogonal Projection Based on Kronecker Product
We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications.
Federated Learning: Strategies for Improving Communication Efficiency
We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model.
Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error.
Distributed Mean Estimation with Limited Communication
Motivated by the need for distributed learning and optimization algorithms with low communication cost, we study communication efficient algorithms for distributed mean estimation.
Learning Discriminative and Transformation Covariant Local Feature Detectors
Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi by defining the concepts of "standard patch" and "canonical feature" and leverage these to train a novel robust covariant detector.
Learning discriminative and transformation covariant local feature detectors.
Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi [8] by defining the concepts of “standard patch” and “canonical feature” and leverage these to train a novel robust covariant detector.
Learning Spread-out Local Feature Descriptors
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors.
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.
AdaCliP: Adaptive Clipping for Private SGD
Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models have been proposed.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Pre-training Tasks for Embedding-based Large-scale Retrieval
We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.
Federated Learning with Only Positive Labels
We consider learning a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a single class.
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all labels into account.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating server.
Automatic Engineering of Long Prompts
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex open-domain tasks, guided by comprehensive instructions and demonstrations provided in the form of prompts.
