A simpler 3D Möbius strip design without twists.
problem Creating a Möbius strip without twists.
method A simple rational function on a polynomial subset of R^2.
result The new design is a union of a segment and a graph of a rational function.
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48 results for “simpler design”
Automates molecule design with simpler SMILES generation and reinforcement learning.
problem Designing molecules with specific chemical properties.
method Combines context-free grammar for SMILES strings and reinforcement learning with a Transformer model.
result Significantly reduces model steps per atom and beats previous baselines.
Automated HPO design using Bayesian optimization and benchmarking.
problem Designing effective hyperparameter optimization algorithms is manual and lacks systematic understanding.
method Formalized space of HPO candidates, Bayesian optimization for search, ablation analysis.
result Simple configurations can perform well in HPO, especially with right parameters.
Deep learning system generates new Chinese fonts via style variables.
problem Efficiently design new Chinese fonts.
method End-to-end deep learning system generating new style fonts via interpolation of latent style-related embedding variables.
result Smooth transition between different font styles achieved.
New algorithms optimize faster for simpler problems in machine learning.
problem Design faster optimization algorithms for practical machine learning problems.
method Adaptive gradient algorithms with refined conditions based on Hessian singular values.
result Achieved optimal gradient complexity for linear regression.
Transformers prefer simpler explanations in hierarchical tasks.
problem Navigating tasks with varying complexity levels.
method Well-controlled testbeds based on Markov chains and linear regression.
result Transformers favor the least complex sufficient explanation when presented with simpler data.
Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multi…
We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem where conditions on the design matrix complicate the analysis. A clearer and simpler picture emerges by studying the No…
Sharp bounds on ERM's minimal error in regression.
problem Understanding ERM's performance in regression tasks.
method Sharp lower bounds for ERM in random and fixed design settings.
result ERM's performance depends on the global or local complexity of the model.
The paper optimizes designs for distinguishing between Gaussian process models.
problem Discriminating between two Gaussian process models.
method Sequential and static design criteria, including Kullback Leibler divergences and log-likelihood ratios.
result Necessary conditions for optimal design measures are provided.
Improved model for multivariate time series prediction with simpler architecture.
problem Multivariate probabilistic time series prediction challenges.
method Simplified transformer-based attentional copulas (TACTiS) with linearly scalable parameters.
result Significantly better training dynamics and state-of-the-art performance.
Low-connectivity reservoirs outperform standard designs in chaotic system forecasting.
problem Forecasting chaotic systems with high accuracy and low computational resources.
method Used Bayesian optimization to find optimal reservoir configurations, focusing on global system climate rather than short-term prediction.
result Optimized reservoirs with very low connectivity perform well in forecasting chaotic systems, challenging existing design heuristics.
FTRL algorithm with negative entropy regularizer achieves best-of-three-world results for linear bandits.
problem Designing an FTRL algorithm for linear bandits with optimal regret bounds.
method Follow-the-regularized-leader (FTRL) algorithm with negative entropy regularizer.
result Regret bounds achieve the same or nearly the same order as detect-switch type algorithm but with simpler design.
How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment? We show that current approaches to penalizing side effects can introduce bad incentives, e.g. to prevent any irreversible changes in the environment, including the actions of other agents. To isolate the source…
A simpler proof for apex graphs in McCarty and Thomas' conjecture.
problem Proving a conjecture about apex graphs and their linklessly embeddable properties.
method Shorter and simpler proof for the apex case.
result A shorter and simpler proof for the apex case of the conjecture.
Benchmark proposes to assess molecule docking efficiency.
problem Lack of realistic benchmarks for measuring progress in drug design.
method Proposes a docking-based benchmark using SMINA software.
result Graph-based generative models fail to generate high-scoring molecules.
Modern ML methods show unexpected behaviors that contradict classical statistics.
problem Modern machine learning methods exhibit behaviors at odds with classical statistical intuitions.
method Comparison between fixed and random design settings in ML and statistics.
result Moving from fixed to random designs reveals new insights into bias-variance tradeoffs and overfitting.
Simpler algorithm learns shallow networks faster.
problem Learning a linear combination of ReLU activations.
method A simpler one-stage algorithm with improved runtime.
result Runs in time.
Orders and Fiberingmath.GR
Simpler proof for Kielak's virtual fibering criterion.
problem Virtual fibering criterion for RFRS groups
method Simpler proof
result Simplified proof of Kielak's criterion
New approach for algorithms that learn predictors to improve performance.
problem Improving algorithm performance by learning better predictions.
method Identify functional dependence and apply online learning techniques.
result Improved results in various online settings with simpler analysis.
The paper explores local-correlation models for pricing complex financial contracts.
problem Calibrating synthetic quanto forward contracts and composite options.
method Design on-line calibration procedures for local and stochastic volatility models.
result Calibration performance of local-correlation models compared to simpler approximations.
New model predicts video sequences with latent dynamics.
problem Predicting video sequences with inherent uncertainty.
method Introduces a novel stochastic temporal model with latent dynamics.
result Latent model outperforms prior state-of-the-art methods.
sktime provides a unified interface for time series machine learning.
problem Handling diverse time series learning tasks.
method Unified API for scikit-learn compatible time series tasks.
result Unified approach simplifies time series machine learning.
Quantum Curriculum Learningquant-ph
Q-CurL optimizes quantum learning with a curriculum design.
problem Efficiently training quantum models with limited resources.
method Quantum curriculum learning framework.
result Q-CurL enhances training convergence and generalization.
Efficient DL reduces EM nanostructure design complexity.
problem Designing and optimizing electromagnetic nanostructures efficiently.
method Autoencoder-based dimensionality reduction for one-to-one problem reformulation.
result Significant reduction in computational complexity for EM nanostructures.
Automated neural network synthesis engine for custom designs.
problem Difficulty in creating custom neural networks due to lack of skills and time.
method Automated synthesis engine NeuNetS for text and image domains.
result Builds neural networks faster and with similar accuracy to human-designed models.
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spacescs.LG
Framework simplifies vision-based control and goal discovery.
problem Learning proportional control from visual data.
method Introduces NewtonianVAE for proportional control and goal discovery.
result Dramatic simplification and acceleration of vision-based controllers.
The Lasso method is analyzed for high-dimensional regression with Gaussian designs, leading to new insights on its performance.
problem Analyzing the Lasso method for high-dimensional regression with Gaussian designs.
method Generalizing the Lasso characterization to Gaussian correlated designs with non-singular covariance structure.
result Establishing non-asymptotic bounds on the distance between the distribution of various quantities in the two models.
modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility p…
Pruned Hurwitz numbersmath.GT
We define a new Hurwitz problem which is essentially a small core of the simple Hurwitz problem. The corresponding Hurwitz numbers have simpler formulae, satisfy effective recursion relations and determine the simple Hurwitz numbers. We also apply this idea of finding a smaller simpler enumerative problem to orbifold H…
Hierarchical video model learns actions at coarse and fine levels.
problem Understanding complex video actions at different levels of detail.
method A hierarchical architecture that learns coarse-grained to fine-grained actions.
result Models that learn at multiple levels of granularity perform best.
Simpler proof for quaternionic Calabi conjecture
problem Quaternionic Monge-Ampère equation on compact hyperKähler manifolds
method Proves C0 estimate for quaternionic Monge-Ampère equation
result Simpler proof for quaternionic Calabi conjecture
We provide an alternative, simpler proof of the existence of thick triangulations for noncompact manifolds. Moreover, this proof is simpler than the original one given in \cite{pe}, since it mainly uses tools of elementary differential topology. The role played by curvatures in this construction is also…
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term. We design new algorithms that achieve regret …
Novel PO algorithms improve LLM alignment tasks.
problem Evaluating PO algorithms on LLM alignment is costly and noisy.
method Designed a diagnostic suite of MuJoCo tasks and datasets, used evolutionary strategies to discover specialized PO algorithms.
result Proposed MPO algorithms significantly outperform existing PO algorithms in LLM alignment.
Reduces constructing multiplicative connections to simpler tasks.
problem Constructing multiplicative connections on proper Lie groupoids.
method Reduction to simpler tasks involving proper and regular Lie groupoids.
result Simpler methods for constructing multiplicative connections.
SGD tends to favor simpler subnetworks, improving generalization.
problem SGD's tendency to favor simpler subnetworks over complex ones.
method Identifying invariant sets and analyzing SGD's behavior around them.
result SGD collapses networks to simpler subnetworks, improving generalization.
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances…
On Raymond-Williams' examplemath.GT
Simplified construction of p-adic transformation group action.
problem Constructing an action of p-adic integers on a compactum.
method Simpler construction of the p-adic transformation group action.
result A simpler example of p-adic transformation group action.
Paper develops a method to automatically create learning curricula for reinforcement learning.
problem Designing an effective sequence of simpler tasks to prepare for a more complex task.
method Formulated as a Markov Decision Process, extended to handle multiple transfer learning algorithms.
result A curriculum policy can be learned from experience, improving training speed for reinforcement learning agents.
New geometric regularizers improve deep learning generalization.
problem Improving deep learning models' ability to generalize to unseen data.
method Using Bregman divergence loss and bounded spectral products, we propose a novel geometric regularizer to enhance model generalization.
result Good generalization can be achieved by designing deep models with specific structural regularizers.
Simpler proof for non-basic sets in 2D.
problem Proving non-basic sets in 2D.
method Defining Sternfeld arrays and proving non-basic sets.
result Simpler proof of non-basic sets in 2D.
Simplified proof for Frank and Lieb's inequality on Heisenberg group.
problem Proving the sharp Frank-Lieb inequality on the Heisenberg group.
method Simpler proof based on 2nd variation of subcritical functionals.
result A simpler proof of the inequality without the need for minimizer existence.
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even to similar domains. To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, s…
Defines basic sections of LA-groupoids for simpler modeling.
problem Modeling sections of stacky Lie algebroids.
method Introduces basic sections with injective core-anchor map.
result Basic sections are Morita invariant and equivalent to multiplicative sections.
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain. We provi…
Paper offers a simpler solution for managing complex financial options.
problem Managing a large number of financial assets with diverse dynamics.
method Developed a simple analytical approximation for market making.
result Shows significant flexibility over existing market making strategies.
ASVRG accelerates stochastic variance reduction methods with simplicity and efficiency.
problem Efficiently solving convex and non-convex optimization problems.
method Accelerated proximal stochastic variance reduced gradient (ASVRG) method with momentum acceleration.
result ASVRG achieves best known oracle complexities for strongly and non-strongly convex objectives.