A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study investigates one-shot semi-supervised learning for image classification.
CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
One-shot algorithm for feature-distributed kernel PCA reduces communication costs.
Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step requ…
Deep learning, even if it is very successful nowadays, traditionally needs very large amounts of labeled data to perform excellent on the classification task. In an attempt to solve this problem, the one-shot learning paradigm, which makes use of just one labeled sample per class and prior knowledge, becomes increasing…
Less-than-one-shot learning tackles few-shot learning with minimal data.
CGMMD generates conditional samples in one shot with MMD and nearest neighbors.
Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the search space and evaluating the most promising architectures. However, it is computationally very costly. As a remedy, the one-shot approach ha…
Learning β for k-SAT with one sample is hard, especially for low degrees.
Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We…
FROST speeds up and stabilizes one-shot semi-supervised learning.
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot", vast computational effort is invested for simulating these system in small steps, e.g., using Molecular…
Study reduces NAS search cost by generating multiple complex architectures in one shot.
BOSS learns from one labeled sample per class to match fully supervised performance.
Federated learning supports exact support recovery with minimal communication.
The ability to rank candidate architectures is the key to the performance of neural architecture search~(NAS). One-shot NAS is proposed to reduce the expense but shows inferior performance against conventional NAS and is not adequately stable. We investigate into this and find that the ranking correlation between archi…
A practical one-shot federated learning algorithm for cross-silo setting.
Designs efficient algorithms for online and sliding window models of subspace embeddings for all p.
We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine then sends an -length message to a server, at which a parameter minimizing an expected loss is to be estimated. We propose an alg…
Inverse Drum Machine separates drum mixes using transcription and synthesis.
This work improves convergence guarantees for unadjusted HMC in KL and Rényi divergences.
New method for one-shot timbre transfer in music.
GraphBSI generates graphs by refining a belief in continuous space, outperforming existing models.
One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. Nevertheless, there is still a lack of understanding on how these weight-sharing algorithms exactly work due to the many factors controlling the dynamics of the process. In order to allow a sc…
A method to reduce memory usage in NAS by pruning the search space.
Efficient algorithm finds fast Transformer models.
FedFisher improves one-shot FL by using Fisher information.
Optimal SD improves ridge regression performance strictly and precisely.
Paper proposes a robust method for federated ICA with geometric median aggregation.
SPECTRE uses spectral conditioning to generate larger graphs without mode collapse.
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -…
One-shot federated learning method for prediction sets.
We develop DTs for PDE models using KL-NN and TL, analyzing TL's moment equations and one-shot learning for exactness.
We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast wei…
One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label …
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bay…
This study improves UAV identification using RF signals with one-shot generative methods.
Neural network models that are not conditioned on class identities were shown to facilitate knowledge transfer between classes and to be well-suited for one-shot learning tasks. Following this motivation, we further explore and establish such models and present a novel neural network architecture for the task of weakly…
Proposes a method to adapt to new classes in a domain shift.
We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine sends a -bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an exp…
We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minimum margin amongst the bootstrapped models. This extends standard margin sampling in a way that increases its diversity in a supervised manne…
The emergence of neural architecture search (NAS) has greatly advanced the research on network design. Recent proposals such as gradient-based methods or one-shot approaches significantly boost the efficiency of NAS. In this paper, we formulate the NAS problem from a Bayesian perspective. We propose explicitly estimati…
Bonsai-Net efficiently discovers state-of-the-art models with fewer parameters.
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from…
One-Shot Neural architecture search (NAS) attracts broad attention recently due to its capacity to reduce the computational hours through weight sharing. However, extensive experiments on several recent works show that there is no positive correlation between the validation accuracy with inherited weights from the supe…
Quantum federated learning improves with non-IID data using one-shot communication.
Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identification from noisy spectra without the need for additional preprocessing. However, their application in practical spec…
We present a compositional embedding framework that infers not just a single class per input image, but a set of classes, in the setting of one-shot learning. Specifically, we propose and evaluate several novel models consisting of (1) an embedding function f trained jointly with a "composition" function g that compute…