The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to efficiently process information at the edge. A key challenge, however, is to understand which learning rules are best suited for specific t…
On-device research index
arXiv research
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.
168,878 papers · 148 categories
Trend · papers per month
48 results for “versatile architecture”
Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, pre…
This work proposes a RL approach to learn versatile robotic manipulation tasks.
problem Challenging manipulation tasks in robotics and vision.
method Reinforcement learning (RL) to combine primitive skills, no intermediate rewards, few demonstrations, and efficient skill learning.
result Versatile robotic manipulation in challenging settings with temporary occlusions and dynamic scene changes.
Geometric Algebra Transformer (GATr) handles various geometric data types efficiently.
problem Lack of a single architecture for diverse geometric data types.
method GATr uses projective geometric algebra, equivariant to E(3), and is a Transformer architecture.
result GATr outperforms non-geometric and equivariant baselines in various geometric tasks.
Optuna introduces a new hyperparameter optimization framework.
problem Efficient hyperparameter optimization for machine learning models.
method Define-by-run API, efficient implementation, versatile architecture.
result Optuna is the first software meeting next-generation hyperparameter optimization criteria.
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
A new framework generates large hierarchical search spaces for neural architectures.
problem Discovering neural architectures from simple blocks is hard.
method Context-free grammars for a unified, scalable search space.
result Efficiently searches over complete architectures, outperforming existing methods.
New origami structures adapt to over 100 shapes with minimal actuation.
problem Limited shape-morphing capabilities in metamaterials and robotics.
method Hierarchical origami based on polyhedrons, using simple actuation.
result Single structure adapts to over 103 configurations with few actuations.
UPR hybrid model improves phase retrieval performance.
problem Recovering signals from phase-less measurements.
method Model-based data-driven deep architecture (UPR).
result UPR shows potential in improving phase retrieval.
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by levera…
einspace expands NAS search space to include diverse neural architectures.
problem NAS results are often limited to existing structures; new designs are rare.
method einspace uses a probabilistic context-free grammar to create a versatile search space.
result einspace discovers novel and improved architectures, including convolutions and attention.
This paper compares two GAN architectures to reduce mode collapse.
problem Reducing mode collapse in Generative Adversarial Networks (GANs).
method Explains and compares PacGAN and VEEGAN models.
result PacGAN performs slightly better than vanilla GAN in terms of mode collapse.
MetaPerturb learns to improve generalization across different tasks and architectures.
problem Improving generalization on unseen data for diverse tasks and architectures.
method MetaPerturb is a meta-learned set-based perturbation function that improves generalization performance across heterogeneous tasks and architectures.
result MetaPerturb significantly outperforms baselines on most tasks and architectures with minimal increase in parameter size and no hyperparameters to tune.
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and pre- dictions, even of the most complex neural network …
Proposes a neural network for efficient deep hedging strategies.
problem Hard training of optimal hedging strategies due to action dependence.
method Introduces no-transaction band network, a neural architecture.
result Demonstrates faster and more precise hedging strategies.
Proposes a new multi-scale architecture for generative flows to improve log-likelihood and sampling quality.
problem Challenges of high-dimensional latent space in flow models.
method Data-dependent dimension factorization based on likelihood contribution heuristic.
result Improvements in log-likelihood score and sampling quality on image benchmarks.
When using reinforcement learning (RL) algorithms it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on an agent's performance, however, and determining a suitable approximation arch…
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both pred…
NATS-Bench benchmarks NAS algorithms for architecture topology and size.
problem Incomparable performance of NAS algorithms due to different search spaces and training setups.
method Unified benchmarking platform for architecture topology and size searching.
result Validated benchmark for 15,625 topology and 32,768 size candidates.
The paper explores how structured representations influence learning dynamics in neural networks.
problem Understanding the training dynamics of deep neural networks.
method Investigates a family of enriched transformation layers with constrained pathways and adaptive corrections.
result Improved robustness, smoother optimization, and scalable depth behavior are achieved through structured representations.
L-GATr transforms high-energy physics data using geometric algebra and Lorentz symmetry.
problem Extracting scientific understanding from particle-physics experiments with high precision and efficiency.
method L-GATr, a geometric algebra Transformer, representing data in 4D space-time and being equivariant under Lorentz transformations.
result L-GATr achieves performance comparable to or better than domain-specific baselines on regression, classification, and generative tasks.
U-Nets use belief propagation for efficient image denoising and classification.
problem Efficiently denoise and classify images using generative hierarchical models.
method Interpreted U-Nets as implementing belief propagation in tree-structured models.
result U-Nets can efficiently approximate denoising functions with sample complexity bounds.
We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link…
Paper proposes MCC to reduce class confusion for versatile DA.
problem Class confusion in DA methods limits their performance across different scenarios.
method Introduces Minimum Class Confusion (MCC) loss function to handle various DA scenarios.
result MCC significantly improves performance on diverse DA scenarios, including Multi-Source and Multi-Target DA.
Proposes a new neural network architecture combining MLP and basis functions.
problem Function approximation and operator learning in scientific machine learning.
method Combines robust MLP inner functions with flexible basis functions outer functions.
result KKAN outperforms MLPs and KANs in function approximation and operator learning tasks.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarioscs.LG
Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space. In this work, a method known as "Versatile Auxiliary Classifier with Generative Adversarial Network" for multi-class scenarios is presented. …
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, …
Versatile linkage: a family of space-conserving strategies for agglomerative hierarchical clusteringstat.ME
Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized mean…
Theoretical guarantees for neural estimators in parametric statistics are derived.
problem Lack of theoretical guarantees for neural estimators in parametric statistics.
method Decompose risk into terms and verify assumptions for convergence.
result Derive theoretical guarantees for neural estimators.
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof. We derive random hinge…
BSA-TNP improves NP scalability and accuracy for spatiotemporal data.
problem Scalability and accuracy trade-off in Neural Processes.
method Introduces KRBlocks, group-invariant attention biases, and BSA for scalable spatiotemporal inference.
result BSA-TNP matches or exceeds accuracy of best models while training faster.
Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as age and gender to model patient cohort relations in a graph structure. Concepts from graph signal processing are leveraged to learn the opti…
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
Unveiling the Power of Uncertainty: A Journey into Bayesian Neural Networks for Stellar datingastro-ph.IM
Bayesian neural networks improve stellar age predictions with reduced uncertainty.
problem Handling uncertainties in stellar dating using complex data relationships.
method Hierarchical Bayesian architecture with neural networks for probabilistic modeling.
result Age predictions with reduced uncertainty and mean absolute error < 1 Ga.
CRF model improves protein secondary structure prediction.
problem Improving secondary structure prediction of proteins.
method Applied Conditional Random Fields (CRF) to protein classification.
result CRF model leads to extremely accurate protein secondary structure predictions.
VeLO learns versatile optimizers from deep learning tasks.
problem Training deep learning models with hand-designed optimizers.
method Meta-training a neural network optimizer on a wide variety of optimization tasks.
result The learned optimizer automatically adapts to different optimization tasks without hyperparameter tuning.
LaT-PFN model predicts time series with zero-shot capability.
problem Zero-shot time series forecasting.
method In-context latent space learning with JEPA and PFN integration.
result Superior zero-shot predictions compared to baselines.
TACTiS models time series uncertainty with transformer attention.
problem Estimating predictive uncertainty in high-dimensional multivariate time series.
method Transformer-Attentional Copulas using attention-based decoder.
result TACTiS produces state-of-the-art predictions on real-world datasets.
A benchmark evaluates ioUS-to-MR synthesis methods for brain tumor surgery.
problem Difficult interpretation of ioUS images for brain tumor surgery.
method Six generators trained under four inference regimes and two targets on public data.
result SynDiff-2.5D best preserved downstream segmentation (U_Dice=0.55).
Deep neural networks for structured prediction using kernel-induced losses.
problem Structured prediction tasks for images and texts.
method Designing a novel family of deep neural architectures that predict in a finite-dimensional subspace derived from the kernel-induced loss.
result Gradient descent algorithms can be used for structured prediction with deep neural networks.
A new deep learning model improves phase retrieval performance.
problem Recovering signals from phaseless measurements.
method Hybrid model-based data-driven deep architecture (Unfolded Phase Retrieval, UPR).
result Significant improvement in phase retrieval performance.
Paper learns versatile balancing and recovery motions for humanoid robots.
problem Training humanoid robots to handle unexpected perturbations.
method Hierarchical Deep Reinforcement Learning in a physics simulator.
result Learned skills comparable to preprogrammed controllers but more adaptable.
TEAFormers preserve multi-dimensional time series structures for better forecasting.
problem Traditional Transformers flatten multi-dimensional time series data, losing critical multi-dimensional relationships.
method Tensor-Augmented Transformer (TEAFormer) with Tensor-Augmentation (TEA) module.
result Significant performance enhancements in time series forecasting across benchmarks.
Paper infers human mobility from sparse trajectories.
problem Modeling and inferring human mobility from sparse trajectory data.
method Proposes a single trajectory inference algorithm and a deep learning architecture for multiple trajectories.
result Deep learning model achieves 2x overall accuracy improvement on sparse trajectories.
Unified model improves object-centric world modeling with new abilities.
problem Integration of recent advances and temporal imagination abilities.
method Generative Structured World Models (G-SWM) framework.
result G-SWM achieves best or comparable performance in temporal generation.
New method uses MCTS only at test time for faster game learning.
problem High computational demands in game learning.
method Combines MCTS with TD learning agents for faster, reproducible agents.
result First learning-from-scratch agent to beat Edax up to level 7.
SpodNet learns SPD matrices with structural constraints.
problem Estimating SPD matrices with additional structural constraints.
method Introduces SpodNet, a neural network module that guarantees SPD outputs and supports structural constraints.
result SpodNet learns SPD and sparse matrices effectively.