Search Results for author:
Found 16 papers, 4 papers with code
Breaking the Curse of Dimensionality with Distributed Neural Computation
This improves the optimal bounds for traditional non-distributed deep learning models, namely ReLU MLPs, which need $\mathcal{O}(\varepsilon^{-n/2})$ parameters to achieve the same accuracy.
Graph Metanetworks for Processing Diverse Neural Architectures
However, those works developed architectures tailored to specific networks such as MLPs and CNNs without normalization layers, and generalizing such architectures to other types of networks can be challenging.
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain.
Optimizing Data Collection for Machine Learning
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect.
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance?
Feature Generation for Long-tail Classification
Oversampling instances of the tail classes attempts to solve this imbalance.
Ranked #1 on
Long-tail Learning
on mini-ImageNet-LT
f-Domain-Adversarial Learning: Theory and Algorithms
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset.
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label.
Self-Supervised Real-to-Sim Scene Generation
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
Ultrahyperbolic Representation Learning
In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines.
Video Face Clustering with Unknown Number of Clusters
Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing.
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification.
Deep Spectral Clustering Learning
We derive a closed-form expression for the gradient that is efficient to compute: the complexity to compute the gradient is linear in the size of the training mini-batch and quadratic in the representation dimensionality.
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data
Classic approaches alternate the optimization over the learned metric and the assignment of similar instances.
Closed-Form Training of Mahalanobis Distance for Supervised Clustering
Clustering is the task of grouping a set of objects so that objects in the same cluster are more similar to each other than to those in other clusters.
Fantope Regularization in Metric Learning
This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.
