A new MDS method uses derivative-free optimization for manifold learning.
problem Learning the intrinsic geometry of high-dimensional manifolds.
method Pattern Search Multidimensional Scaling (PS-MDS) using General Pattern Search (GPS) framework.
result PS-MDS accurately infers manifold geometry in clean and noisy synthetic datasets.
Analyzes Lévy flights on manifolds for finding small targets.
problem Finding small targets using Lévy flights on various manifolds.
method Analytic description of Lévy flights on closed Riemannian manifolds, including asymptotics of expected stopping time.
result Computes the expected time for finding a small target by Lévy flight on surfaces.
Algorithm finds knot friends in 3-manifolds.
problem Identifying knots that share similar 3-manifold complements.
method Developed an algorithm using SnapPy and Regina.
result Constructed a census of simple knots with friends.
We introduce a method that uses the Cauchy-Crofton formula and a new curvature formula from integral geometry to reweight the sampling probabilities of Metropolis-within-Gibbs algorithms in order to increase their convergence speed. We consider algorithms that sample from a probability density conditioned on a manifold…
Stochastic gradient descent on manifolds improves low-rank approximation.
problem Efficiently approximate large matrices with lower rank.
method Stochastic gradient descent on a manifold.
result Algorithm outperforms Euclidean space methods on Netflix Prize data.
Cover trees speed up MRI fingerprint recovery by reducing computation.
problem Efficiently reconstructing MRI fingerprint signals from compressed sensing data.
method Use cover trees for fast approximate nearest neighbor searches in IPG algorithm.
result Achieves 2-3 orders of magnitude reduction in computations.
Derives a method to optimize high-dimensional functions on low-dimensional manifolds.
problem High-dimensional derivative-free optimization with high sample complexity.
method Online learning approach that learns the manifold while optimizing the function.
result Significantly reduces sample complexity compared to existing methods.
NLGS optimizes latent geometry for better model performance.
problem Improving machine learning model performance by aligning latent space geometry with data structure.
method NLGS uses product manifolds with Gromov-Hausdorff distance for latent geometry search.
result NLGS finds optimal latent geometry with query-efficient Bayesian optimization.
Novel co-learning method for manifolds with group actions using multiple fibre bundles.
problem Learning from manifolds with group actions without labeled data.
method Representation theory to associate multiple fibre bundles, leveraging group actions for unsupervised learning.
result Improved robust nearest neighbor search and community detection on cryo-electron microscopy images.
Research on knots and their 4-manifold covers.
problem Determining if knots are slice.
method Analyzing double branched covers and computer searches.
result Methods to decide if knots are slice.
Upper bound found for graph manifold complexity.
problem Determining complexity of graph manifolds.
method Using Matveev complexity for all catalogued manifolds.
result Upper bound is sharp for 14,502 catalogued manifolds.
A new framework improves graph construction for semi-supervised learning.
problem Improving graph construction for better semi-supervised classification accuracy.
method Parallel hyperparameter search with adaptive resource allocation for gradient-based optimization of edge weights.
result Significantly outperforms existing graph construction schemes in accuracy and scalability.
BOOOM optimizes orthonormal matrices without needing gradients.
problem Optimizing over the Stiefel manifold in non-convex, non-smooth settings.
method Global Givens rotation-based parametrization and Recursive Modified Pattern Search.
result BOOOM achieves strong performance across various optimization problems.
A new language for neural architecture search decouples search spaces and algorithms.
problem Current neural architecture search methods are limited to specific use-cases and lack general-purpose constructs.
method Proposes a formal language for encoding search spaces over general computational graphs, allowing modular, composable, and reusable encodings.
result The language enables easy experimentation with different search spaces and algorithms without reinventing the wheel.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
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.
A new Riemannian algorithm reduces variance in manifold optimization.
problem Optimizing functions on manifolds with stochastic gradient descent.
method Riemannian stochastic variance reduction with retraction and vector transport.
result The proposed algorithm outperforms standard methods on SPD and Grassmann manifolds.
MICO uses mutual information co-training to improve selective search efficiency.
problem Efficiently search and route unseen queries in large-scale search systems.
method Mutual Information Co-training framework for selective search with minimal supervision.
result Significantly improves performance on multiple metrics of selective search.
This work recommends personalized search stories to users based on their interests.
problem Personalized search story recommendation within search engines.
method Deep reinforcement learning architecture trained by imitation learning and reinforcement learning.
result Empirically demonstrated effectiveness on real-world data sets.
Paper generalizes control contraction metrics to Finsler geometry.
problem Designing nonlinear controllers for complex geometries.
method Generalization of CCMs to Finsler geometry, providing open loop and sampled data controllers.
result Simplified computation of sampled data control without real-time shortest path computation.
A locally conformally Kähler (LCK) manifold is a manifold which is covered by a Kähler manifold, with the deck transform group acting by homotheties. We show that the search for LCK metrics on Oeljeklaus-Toma manifolds leads to a (yet another) variation on Kronecker's theorem on units. In turn, this implies that on Oel…
A method to reduce memory usage in NAS by pruning the search space.
problem High GPU memory consumption in One-Shot NAS techniques.
method Utilising Zero-Shot NAS to prune the search space before applying One-Shot NAS.
result Reduces memory consumption by 81% while maintaining accuracy.
Paper uses CMAB to improve NAS efficiency and accuracy.
problem Improving efficiency and accuracy of NAS for DNNs.
method Formulated NAS as CMAB, used Nested Monte-Carlo Search.
result Discovered cell structure achieves comparable accuracy to state-of-the-art, 20x faster.
New agent learns from previous search spaces to improve NAS efficiency.
problem NAS requires restarting learning from scratch between different search spaces.
method Transformer-based agent for joint training and knowledge transfer.
result Efficient knowledge transfer between search spaces improves NAS performance.
KPCA-BO improves BO for high-dimensional optimization problems by learning a non-linear sub-manifold.
problem High-dimensional optimization problems where Gaussian Process regression requires too much data and computation.
method KPCA-BO embeds a non-linear sub-manifold in the search space, learning a GPR model on this sub-manifold.
result KPCA-BO outperforms vanilla BO in convergence speed, especially as dimensionality increases.
PGS uses neural networks to improve policies online without search trees.
problem Limited scalability of Monte Carlo Tree Search (MCTS) for high branching factor games.
method Adapts a neural network simulation policy via policy gradient updates, avoiding search trees.
result PGS achieves comparable performance to MCTS and defeats strong Hex agents.
New method reduces NAS search time and complexity.
problem High computational cost and complexity in NAS.
method Differentiable search space with annealing and pruning.
result Achieves 1.68% error on CIFAR-10 with 0.2 GPU days.
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring…
FiGS searches over a larger space of architectures for efficient mobile models.
problem Designing small, efficient deep networks for mobile devices.
method Differentiable search method using sparse regularization and Logistic-Sigmoid distribution.
result FiGS produces state-of-the-art parameter-efficient models on ImageNet and improves object detection performance.
This work proposes searching for optimal operation distribution in neural architecture search.
problem Finding optimal neural architecture with specific operations and connections.
method Search for the optimal operation distribution, providing a stochastic and approximate solution.
result Operation distribution holds enough discriminating power to reliably identify a solution and is easier to optimise than traditional encodings.
CrossBeam learns to search more efficiently in program synthesis.
problem Efficiently searching through vast program spaces.
method Trains a neural model to guide program synthesis, combining previously explored programs.
result CrossBeam explores much smaller portions of the program space compared to state-of-the-art methods.
Deep-n-Cheap automates deep learning model search for low complexity.
problem Finding efficient deep learning models for various datasets.
method Automated search framework for architecture and hyperparameters, including search transfer.
result Models offer comparable performance to state-of-the-art but are faster to train.
Paper presents a new trie for integer sketches to improve similarity searches.
problem Efficient similarity searches on integer sketches.
method Introduces a novel b-bit sketch trie that leverages succinct data structures. result Significantly improves search time and space-efficiency of similarity searches.
Improves diffusion model performance and efficiency through classical search.
problem Tackles inference-time control in diffusion models.
method Proposes a framework combining local and global search for efficient navigation.
result Significant gains in performance and efficiency across various domains.
We give a construction of hyperbolic 3-manifolds with rank two fundamental groups and report an experimental search to find such manifolds. Our manifolds are all surface bundles over the circle with genus two surface fiber. For the manifolds so obtained, we then examine whether they are of Heegaard genus two or not. As…
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Graph-based NAS improves sample efficiency in architecture design.
problem Current NAS search spaces are static sequences, limiting expressiveness.
method Proposed graph-based search space with vertices and edges for iterative and branching decisions.
result Graph representation improves sample efficiency in architecture design.
Random search outperforms leading NAS methods in neural architecture search.
problem Improving the reproducibility and efficiency of neural architecture search.
method Proposed random search with early-stopping and weight-sharing as NAS baselines.
result Random search with weight-sharing achieves state-of-the-art results on PTB and highly competitive results on CIFAR-10.
MNMS learns from previous tasks to speed up model search for new tasks.
problem NAS requires extensive manual design and optimization for each new task.
method MNMS uses reinforcement learning to condition model construction on previously successful searches.
result MNMS can conduct simultaneous searches for multiple tasks and transfer knowledge to new tasks.
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.
Paper improves full-text search engines for fast exact NNS in binary codes.
problem Efficient nearest neighbor search in Hamming space for full-text search engines.
method Revisits and combines three techniques from information retrieval: bit operation, subs-code filtering, and data preprocessing with permutation.
result Significant speed-ups for NNS in binary codes over state-of-the-art term match approach.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure guided by a learned cost function, and then returning the least cost output uncovered during the search. This framewor…
Solves the Dirichlet problem for minimal hypersurfaces on Riemannian manifolds.
problem Finding conditions for solvability of the Dirichlet problem with Lipschitz boundary data.
method Extends Williams' result to Riemannian manifolds, focusing on smallness conditions.
result Establishes existence of solutions under certain boundary data conditions.
SGAS improves neural architecture search by choosing and pruning operations greedily.
problem NAS often fails to generalize in final evaluation.
method Divides search into sub-problems and chooses/prunes candidate operations greedily.
result SGAS finds state-of-the-art architectures with minimal computational cost.
UNAS combines DNAS and RL for efficient architecture search.
problem Discovering high accuracy or low latency neural architectures.
method Unified framework combining differentiable and reinforcement learning approaches.
result UNAS achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets.
Efficient neural architecture search by sampling structure and operations.
problem Efficiently searching for optimal neural architectures.
method Decouples structure and operation search, using reinforcement learning with policy vectors.
result Significantly improved efficiency compared to traditional methods.