Goal-directed manipulation of representations is a key element of human flexible behaviour, while consciousness is often related to several aspects of higher-order cognition and human flexibility. Currently these two phenomena are only partially integrated (e.g., see Neurorepresentationalism) and this (a) limits our un…
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48 results for “flexible representation”
This paper explores how representation learning can improve design-based causal inference.
problem Estimating causal effects in design-based studies is challenging due to the need for optimal weights.
method The authors propose an end-to-end estimation procedure that learns a flexible representation to minimize the error in choosing a representation.
result The proposed method is competitive in various causal inference tasks and shows promise for improving design-based weights.
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruc…
Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local…
DINo forecasts PDEs with flexible extrapolation and adaptability.
problem Fixed discretizations limit real-world PDE forecasting.
method DINo uses implicit neural representations for continuous-time dynamics.
result DINo outperforms other neural PDE forecasters.
Graph convolutional deep kernel machine learns representations for graph tasks.
problem Limited representation learning in infinite-width neural networks.
method Developed a graph convolutional deep kernel machine as an infinite-width limit.
result Representation learning improves performance for heterophilous node classification tasks.
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.
Flexible embedding framework for diverse data types.
problem Limited applicability of existing embedding learning methods.
method A flexible framework using entity-relation-matrices and sampling mechanism.
result Framework outperforms state-of-the-art approaches in various tasks.
Proposes a flexible method for learning latent causal representations.
problem Limited applicability of existing causal representation learning methods.
method Imposes constraints on function classes and relaxes identifiability conditions.
result Establishes partial identifiability results under weaker conditions.
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are d…
Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. We…
The paper tackles model collapse in GPLVMs by improving kernel flexibility and projection variance.
problem Model collapse in GPLVMs leading to vague latent representations.
method Theoretical analysis of projection variance, integration of SM and RFF kernels, and variational inference.
result The advisedRFLVM outperforms competing models in informative latent representations and missing data imputation.
Proposes a new method to learn data representations by modeling sample relations.
problem Lack of rich latent structural information in DAEs.
method Explicitly models and leverages sample relations as supervision for representation learning.
result Significantly improves clustering performance on benchmark datasets.
The Weierstrass representation for minimal surfaces in provides a flexible method for constructing minimal surfaces of arbitrary genus. The topological limitations of minimal surfaces interfere with this providing a more general geometric modeling tool. Minimal surfaces lie in the larger class of harmoni…
Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies an…
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these deep learning methods aim at re-representing symbolic relational data in Euclidean spaces. They offer …
Proposes TCWAE to learn disentangled representations using the Wasserstein Autoencoder.
problem Balancing reconstruction fidelity and disentanglement in learning representations.
method TCWAE (Total Correlation Wasserstein Autoencoder) using different KL estimators.
result Competitive results on data sets with known generative factors, and improved reconstructions on unknown factors.
From Partial Exchangeability to Predictive Probability: A Bayesian Perspective on Classificationstat.ME
Bayesian model improves classification performance with flexible uncertainty modeling.
problem Improving classification performance with flexible uncertainty modeling.
method Combines Gaussian process and Dirichlet process priors for latent function and link function, respectively.
result Outperforms standard logistic regression on simulated data.
Unified approach for interpretable regression with flexible modeling.
problem Combining predictive adaptivity with interpretability in heterogeneous data.
method Combining random Fourier features, spectral feature map, principal component analysis, Gaussian mixture model, and cluster-specific generalized additive models.
result Consistently improves upon classical and black-box models across benchmark datasets.
Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic charac…
Two flexible, degenerate constructions related to Thurston's theorem.
problem Understanding the structure and local non-rigidity of Teichmüller spaces and their representations.
method Constructing geodesic segments and open sets in Teichmüller spaces with specific properties.
result Geodesic segments and open sets with degenerate properties in Teichmüller spaces.
In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying in…
We propose a method to learn causal response representations through direct effect analysis.
problem Uncovering direct causal effects in complex, multivariate settings.
method Our method bridges conditional independence testing with causal representation learning, formulating an optimisation problem to maximise evidence against conditional independence.
result The largest eigenvalue distribution can be bounded by an -distribution, providing testable conditional independence.
Framework adapts to new tasks based on prior knowledge.
problem Models struggle to adapt to novel tasks without direct experience.
method Learned task representations and meta-mappings to transform them.
result Meta-mapping achieves 80-90% performance on novel tasks.
Proposes flexible auto-encoders for varying data dimensions.
problem Fixed latent dimensions limit data flexibility.
method Stochastic bottleneck with weighted dropouts.
result Seamless variable dimensionality reduction with high performance.
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalitiescs.LG
Symile learns joint representations across multiple modalities, outperforming pairwise CLIP.
problem Pairwise contrastive learning fails to capture joint information between multiple modalities.
method Symile uses a flexible, architecture-agnostic objective to learn modality-specific representations by deriving a lower bound on total correlation.
result Symile outperforms pairwise CLIP on cross-modal classification and retrieval across various datasets.
We study the problem of learning permutation invariant representations that can capture "flexible" notions of containment. We formalize this problem via a measure theoretic definition of multisets, and obtain a theoretically-motivated learning model. We propose training this model on a novel task: predicting the size o…
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Frameworkstat.ML
BI-EqNO improves Bayesian inference with flexible neural operators.
problem Inaccurate estimation of marginal likelihoods in approximate Bayesian methods.
method Equivariant neural operator framework for generalized approximate Bayesian inference.
result BI-EqNO enhances both deterministic and stochastic approaches to Bayesian inference.
Enhances source domain knowledge with target data for transfer learning.
problem Limited data in target domains and rigid model assumptions in transfer learning.
method Transfer learning through Enhanced Sufficient Representation (TESR).
result TESR enhances source domain knowledge with target data, improving transfer learning performance.
Flexible VAEs using FIFs improve model likelihood on image datasets.
problem Limitations of diagonal Gaussian posteriors in VAEs.
method Regularized Free-form Injective Flow (FIF) for flexible posterior.
result Full covariance VAEs outperform diagonal Gaussian posteriors.
One popular approach to option pricing in Lévy models is through solving the related partial integro differential equation (PIDE). For the numerical solution of such equations powerful Galerkin methods have been put forward e.g. by Hilber et al. (2013). As in practice large classes of models are maintained simultaneous…
Flexible evidential deep learning improves uncertainty quantification in machine learning.
problem Overconfident predictions in machine learning models can lead to serious consequences.
method Proposes flexible evidential deep learning (F-EDL) to model uncertainty over class probabilities using a flexible Dirichlet distribution.
result Empirically demonstrates state-of-the-art uncertainty quantification performance across diverse scenarios.
Groups of importance in group theory have flexible stability properties.
problem Stability and flexibility of groups in geometric and combinatorial group theory.
method Establishing Kirchberg's Local Lifting Property and Lubotzky--Shalom's Property FD for specific groups.
result Groups like -manifold groups, limit groups, and certain one-relator groups are very flexibly stable.
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's …
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We dem…
We give very flexible, concrete constructions of discrete and faithful epresentations of right-angled Artin groups into higher-rank Lie groups. Using the geometry of the associated symmetric spaces and the combinatorics of the groups, we find a general criterion for when discrete and faithful representations exist, and…
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexi…
Flexible framework for modeling predictive distributions of time series
problem Modeling predictive distributions of nonlinear time series
method Generative adversarial networks
result Direct simulation-based approximation to predictive distributions
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning ap…
Braid groups are an important and flexible tool used in several areas of science, such as Knot Theory (Alexander's theorem), Mathematical Physics (Yang-Baxter's equation) and Algebraic Geometry (monodromy invariants). In this note we will focus on their algebraic-geometric aspects, explaining how the representation the…
We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as t…
C-VAE improves VAE by resolving prior issues and generating better samples.
problem Low-quality samples from VAE due to prior issues.
method Formulates VAE as OT, allows flexible priors, and uses OT formulations.
result C-VAE generates higher quality samples and latent representations.
DeepCTRL integrates rules into deep learning models, allowing flexible control at inference.
problem Lack of flexibility in incorporating rules into deep learning models.
method Integrates rule representations into deep neural networks, enabling flexible control at inference.
result Improves rule verification ratio and accuracy gains at downstream tasks.
SC-InfoNCE improves InfoNCE for feature clustering in contrastive learning.
problem Lack of theoretical understanding of InfoNCE's feature clustering mechanism.
method Introduced a transition probability matrix to model data augmentation dynamics and optimize feature similarity.
result SC-InfoNCE achieves strong performance across diverse domains, aligning feature similarity with downstream data.
We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but …
Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has collected several empirical successes recently, theoretical understanding of its performance was still missing. In a recent work, Arora et al. (2…
HNHN learns from hypergraphs with hyperedge neurons for better classification.
problem Learning from hypergraphs with complex relationships.
method Hypergraph convolution network with hyperedge neurons and adaptive normalization.
result Improved classification accuracy and speed compared to state-of-the-art methods.
PH-VAE models heavy-tailed data with flexible Phase-Type distributions.
problem Standard VAEs fail to capture heavy-tailed behavior in real-world data.
method PH-VAE uses Phase-Type distributions defined by continuous-time Markov chains to adaptively model tail behavior.
result PH-VAE significantly outperforms existing heavy-tail-aware VAEs in approximating diverse heavy-tailed distributions.