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Found 18 papers, 4 papers with code
Uncertainty Estimation for 3D Object Detection via Evidential Learning
We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector.
Neural Spacetimes for DAG Representation Learning
NSTs are implemented as three neural networks trained in an end-to-end manner: an embedding network, which learns to optimize the location of nodes as events in the spacetime manifold, and two other networks that optimize the space and time geometries in parallel, which we call a neural (quasi-)metric and a neural partial order, respectively.
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Mixture-of-Experts (MoEs) can scale up beyond traditional deep learning models by employing a routing strategy in which each input is processed by a single "expert" deep learning model.
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.
