End-to-end models classify composers from musical scores.
problem Classifying composers from musical scores using machine learning.
method Pooled and convolutional architectures for feature extraction.
result Models achieve high accuracy on a large corpus of scores.
Differentiable Algorithm Networks (DAN) enable composable robot learning.
problem Training robots to learn from limited data and imperfect models.
method Composable architecture of neural network modules, each encoding a differentiable robot algorithm and model, trained end-to-end from data.
result DAN modules adapt to one another and compensate for imperfect models and algorithms, achieving best overall system performance.
This work proposes a method to compose visual relations more faithfully.
problem Composing relations between objects in images is challenging due to their entanglement.
method Represent each relation as an unnormalized density (energy-based model) to compose relations factorizedly.
result The proposed method generates and edits scenes with multiple sets of relations more faithfully.
MLJ offers a Julia package for composing machine learning models.
problem Complex model composition in machine learning.
method Flexible model composition tools and meta-algorithms.
result Clear benefits of Julia over multi-language alternatives.
We propose a framework for training GANs on composed data, improving model modularity and interpretability.
problem Training GANs on complex, composed data.
method Composition/decomposition framework for adversarially training GANs on composed data.
result Improves modularity, extensibility, and interpretability of GANs.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
Soft Q-learning improves sample efficiency in robotic manipulation.
problem Limited interaction time in real-world robotic tasks.
method Soft Q-learning for maximum entropy policies, with composability.
result Soft Q-learning policies are more sample efficient and can be composed.
The paper proposes a deep learning technique for structured and composable representations.
problem Learning structured and composable representations from input images and discrete labels.
method End-to-end deep learning to learn representations based on distance estimates between class label and contextual information.
result The representations have a clear structure allowing for class and environment decomposition.
Bardo Composer generates tabletop RPG music based on player speech.
problem Creating immersive background music for tabletop RPGs.
method Speech recognition, emotion classification, and music generation using a novel beam search algorithm.
result Generated music pieces can be accurately identified by human subjects as conveying the intended emotion.
Paper introduces CGPMs for probabilistic data analysis.
problem Difficulty in applying, combining, and comparing different probabilistic techniques.
method Composable generative population models (CGPMs) that extend graphical models and can describe and compose various probabilistic data analysis techniques.
result CGPMs enable efficient and accurate probabilistic data analysis tasks.
Improved code translation by preserving structure with composed fine-tuning.
problem Improving code translation accuracy with unlabeled code outputs.
method Pre-trained denoiser to capture output structure, composed fine-tuning to fine-tune predictor.
result Composed fine-tuning significantly improves generalization over standard fine-tuning.
In this paper, we explicitly construct the Calabi composition of multiple affine hyperspheres possibly including some points viewing as 0-dimensional hypersheres. Then we compute all the basic affine invariants of the composed affine hyperspheres, proving that the composed affine hypersphere is symmetric one if and onl…
Meta-materials simulation sped up with energy surrogates.
problem Challenging simulation of complex meta-materials due to high-fidelity PDEs.
method Learned component-level surrogates using neural networks to model stored potential energy.
result Surrogates enable accurate macroscopic behavior simulation without full structure simulation.
New measure shows how LSTM models compose hierarchical representations.
problem Understanding how LSTM models capture compositional structure in language.
method Novel measure of interdependence between word meanings in LSTM internal gates.
result High interdependence can hurt generalization and reveals hierarchical structure learning.
Relay simplifies deep learning compilation across diverse hardware.
problem Challenges in extending DL frameworks to diverse models and hardware.
method Relay introduces a unified, expressive intermediate representation and extension mechanisms for hardware portability.
result Relay achieves competitive performance across various hardware platforms.
The paper explores fairness in multi-component recommender systems.
problem How to ensure fairness in recommender systems composed of multiple models.
method Study of fairness ranking metrics, theoretical analysis, and empirical evaluation.
result Fairness in recommendation systems can be achieved by improving individual components.
Generative model learns to compose images of objects from different distributions.
problem Capturing complex interactions between objects in scenes.
method Composition-by-Decomposition (CoDe) network.
result Model generates realistic composite images capturing interactions between input objects.
The paper trains a neural network to compose music in a nonlinear, human-like manner.
problem Creating music in a non-chronological, revisiting manner.
method Trained a convolutional neural network with blocked Gibbs sampling to approximate human composition.
result Blocked Gibbs sampling improves sample quality and yields better results than ancestral sampling.
Agents learn action spaces from observation alone.
problem Learning an agent's action space from visual observation.
method Stochastic video prediction and composable action representations.
result Learned representations disentangle action structure.
New method improves data efficiency in reinforcement learning by composing skills.
problem Improving data efficiency in reinforcement learning by composing previously mastered skills.
method Extending policy improvement to maximum entropy framework, introducing successor features, and explicitly learning divergence between base policies.
result Proposes a novel approach that outperforms or matches existing methods in various tasks.
Combines neural networks and graphical models for efficient inference.
problem Efficiently modeling complex structured data.
method Augments graphical models with neural network observation models for inference.
result Scalable algorithm that learns simultaneously with a single objective.
MCP learns reusable skills for complex tasks by combining simple ones.
problem Learning complex tasks with many skills requires impractical amounts of data.
method Factorizes skills into primitives that can be combined multiplicatively.
result MCP can learn and reuse skills for novel tasks from pre-training.
We develop spectral spanners for vectors and use them to create efficient core-sets for determinant maximization.
problem Maximizing determinants of vector sets.
method Spectral spanners and greedy algorithm.
result Almost optimal composable core-sets for determinant maximization.
Paper proposes GAN models for generating multi-track music.
problem Generating music with multiple tracks and temporal dynamics.
method Generative adversarial networks (GANs) for multi-track music generation.
result Models can generate coherent music of four bars right from scratch.
AutoBayes simplifies variational inference by composing models and optimizing them.
problem Generalized variational inference complexities and optimization challenges.
method Compositional framework exploiting chain rules for automatic differentiation.
result Optimized models and parameterized statistical games can be locally optimized.
We analyze the hitting time distributions of stock price returns in different time windows, characterized by different levels of noise present in the market. The study has been performed on two sets of data from US markets. The first one is composed by daily price of 1071 stocks trade for the 12-year period 1987-1998, …
NumPyro accelerates probabilistic programming with JAX transformations.
problem Efficiently handling probabilistic models with hardware acceleration.
method Composable effects and program transformations in JAX.
result Iterative NUTS formulation JIT compiled for speed.
Quantum datasets improve QML performance.
problem Benchmarking QML on classical datasets is uncertain.
method Introduced NTangled dataset of quantum states with varying entanglement.
result QML models trained on NTangled dataset outperform classical models.
Method learns diverse robot skills for complex tasks.
problem Simulation-to-real transfer for robot skills.
method Decomposes skills, learns parameterized embeddings, and composes them.
result Transferable high-level policies using low-level skills.
Paper preserves properties of mappings through generic mappings.
problem Preserving properties of mappings in higher dimensions.
method Composing generic generalized distance-squared mappings.
result Non-singular or injective properties preserved.
It is expected that matter composed of a perfect fluid cannot be at rest outside of a black hole if the spacetime is asymptotically flat and static (non-rotating). However, there has not been a rigorous proof for this expectation without assuming spheical symmetry. In this paper, we provide a proof of non-existence of …
The document proposes a model to measure project risk management maturity.
problem The rapid change in the global environment makes risk management crucial for project success.
method Develops a maturity model to assess organizations' risk management capabilities.
result Measures the effectiveness of organizations in managing project risks.
Paper proposes a method to compose task-agnostic skills for solving new problems.
problem Learning task-specific policies for solving new problems.
method Deep reinforcement learning-based skill transfer and composition method.
result Method transfers skills to new problem settings and solves challenging environments with high data efficiency.
A framework for designing and evaluating new GCN variants.
problem Designing and evaluating new graph convolutional network (GCN) variants.
method Propose a framework to compose networks using building blocks of GCN.
result Several newly composed variants are useful alternatives and competitive with original GCNs.
A Boolean algebra formalizes task composition for reinforcement learning.
problem Formalizing task composition for efficient learning and problem-solving.
method Formalized tasks as a Boolean algebra, learning goal-oriented value functions, and composing them to solve new tasks.
result Agents can solve new tasks without additional learning by composing value functions in specific ways.
Paper shows how to compose mappings to be transverse to subfiber-bundles.
problem Transversality of mappings to subfiber-bundles.
method Generic linearly perturbed mappings and immersions/injections.
result Composing generic linearly perturbed mappings yields transversality to subfiber-bundles.
Generative model learns to create molecules with multiple properties using interpretable substructures.
problem Creating molecules with multiple chemical properties is challenging.
method Compose molecules from substructures identified as responsible for each property, using graph generative models.
result Significant improvements in accuracy, diversity, and novelty of generated compounds over state-of-the-art baselines.
Given a sweepout of a Riemannian 2-sphere which is composed of curves of length less than L, we construct a second sweepout composed of curves of length less than L which are either constant curves or simple curves. This result, and the methods used to prove it, have several consequences; we answer a question of M. Fre…
CoSE models complex drawings by treating strokes as a collection that can be composed.
problem Modeling complex free-form structures like diagrams.
method Generative model using autoencoder to project strokes into a fixed latent space, relational model operates in latent space.
result Model captures both individual strokes and their compositional structure.
Paper optimizes trading profits by predicting price direction using ensemble models.
problem Predicting short-term price movements in financial markets.
method Developed a two-layer ensemble architecture optimized through grid search.
result Improvement of 20% over benchmark strategies.
Paper studies how to combine regret minimizers for solving complex games.
problem Solving large-scale extensive-form games with constraints.
method Derives a calculus for constructing regret minimizers for composite convex sets.
result Local regret minimizers for simpler sets can be combined into an aggregate for composite sets.
New dataset and models generate piano music with coherent structure across multiple timescales.
problem Generating coherent musical structure with neural networks is challenging.
method Used notes as an intermediate representation to model and synthesize music across multiple timescales.
result Trained models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure.
This paper applies AMP theory to improve learning tasks.
problem Improving learning efficiency by optimizing task-specific models.
method Uses aggregated Markov processes to reduce model complexity and enhance learning.
result Demonstrates how AMP theory can be effectively applied to stochastic learning.
New approach learns causally disentangled latent structures in generative models.
problem Fundamental tension between expressivity and structure in latent structure learning.
method Added a context module to an arbitrarily complex model to learn causally disentangled concepts.
result Causally disentangled representations can be composed for out-of-distribution generation.
SurVAE Flows combine VAEs and flows using surjective transformations.
problem Combining the strengths of VAEs and flows to model complex densities.
method Modular framework of composable deterministic and stochastic transformations.
result Exact likelihood computation and lower bound on likelihood.
Agents learn new tasks by combining existing skills in RL.
problem Combining existing skills for new tasks in RL.
method Entropy-regularized RL and standard RL for skill composition.
result Agents can solve new tasks without additional learning.
This paper explores the limits of Transformers in learning new patterns from scratch.
problem Understanding when Transformers can learn new patterns from scratch.
method Introducing the 'globality degree' to measure learnability and developing scratchpad techniques.
result Distributions with high globality cannot be learned efficiently by Transformers.
A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined on a bipartite graph and is an extension of usual restricted Boltzmann machines. A GRBM consists of two different layers: a visible layer composed of continuous visible variables and a hidden layer composed of discrete hidden variables. In th…