We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). However, existing approaches that do so either fail to capture the …
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.
168,695 papers · 148 categories
Trend · papers per month
48 results for “diverse observations”
Identifies latent actions and dynamics from offline data with diverse demonstrators.
problem Recovering latent actions and environment dynamics from action-free trajectories.
method Assumes distinct policies for each demonstrator, identifies latent transitions and policies via matrix factorization.
result Identifies latent transitions and demonstrator policies up to permutation.
Diversity or complementarity of experts in ensemble pattern recognition and information processing systems is widely-observed by researchers to be crucial for achieving performance improvement upon fusion. Understanding this link between ensemble diversity and fusion performance is thus an important research question. …
Diversity and no arbitrageq-fin.PM
A stock market is called diverse if no stock can dominate the market in terms of relative capitalization. On one hand, this natural property leads to arbitrage in diffusion models under mild assumptions. On the other hand, it is also easy to construct diffusion models which are both diverse and free of arbitrage. Can o…
High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.
problem The performance of high-capacity neural network ensembles is often harmed by interventions that promote predictive diversity.
method A large-scale study of nearly 600 neural network classification ensembles, examining various interventions and architectures.
result Discouraging predictive diversity can be benign in large-network ensembles, and higher-capacity models often yield better performance than diverse architectures.
Mixreg improves RL generalization by mixing diverse training environments.
problem Overfitting in RL agents trained in limited environments.
method Trains on a mixture of diverse observations and imposes linearity constraints.
result Mixreg outperforms baselines on unseen testing environments.
The study identifies latent concepts from diverse observations without assuming specific models.
problem Lack of general theoretical support for concept learning.
method Develops a nonparametric framework for identifying latent concepts from multiple classes of observations.
result Correctness guarantees for concept identification without parametric assumptions.
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization…
Kernel sparsity ("dying ReLUs") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless communications, we demonstrate the intersection of coding theory and deep learning through the Grassmannian subspace packing problem in CNN…
New method identifies latent variables without strong assumptions.
problem Recovering latent variables from observational data without strong assumptions.
method Diverse dictionary learning, using set-theoretic intersections, complements, and symmetric differences.
result Identifiability of latent variables up to appropriate indeterminacies without strong assumptions.
In 1999 Robert Fernholz observed an inconsistency between the normative assumption of existence of an equivalent martingale measure (EMM) and the empirical reality of diversity in equity markets. We explore a method of imposing diversity on market models by a type of antitrust regulation that is compatible with EMMs. T…
Training task diversity improves ICL with linear attention.
problem Understanding the impact of training task diversity on in-context learning.
method Modeling training task vectors as a mixture of low-rank Gaussians.
result Our model explains why training with task diversity shortens the ICL plateau and achieves out-of-distribution generalization.
SteinGen generates diverse graph samples from a single example.
problem Generating graphs with characteristic structures and diversity from a single example.
method Combines Stein's method and MCMC with Glauber dynamics and re-estimation of the Stein operator.
result High distributional similarity to the original data, combined with high sample diversity.
Meta-learning performance is affected by how task diversity is allocated, not just overall variability.
problem Meta-learning performance degrades when task diversity is unevenly distributed.
method Decomposed task-specific regression effects into structurally informative and orthogonal components.
result Meta-learning prediction degrades when a larger fraction of task variability is orthogonal and non-informative.
Product diversity of large US firms has declined steadily since 1997.
problem Lack of data on global product diversity makes investigation difficult.
method Text mining of US firms' product descriptions from 1997-2017.
result Product diversity of large US firms has been declining since 1997.
New method recovers diverse policies from expert data using state-action pair weighting.
problem Recovering diverse policies from expert trajectories.
method Pointwise mutual information weighted behavioral cloning.
result Effective in focusing on state-action pairs most representative of the style.
Self-distillation improves model performance by increasing teacher diversity and smoothing predictions.
problem Improving model generalization and performance through self-distillation.
method Interpreting self-distillation as MAP estimation and proposing instance-specific label smoothing.
result Self-distillation enhances model performance by increasing teacher diversity and smoothing predictions.
The highly detailed international trade data among all countries in the world during 1971-2000 shows that the kinds of export goods and the logarithmic GDP (gross domestic production) of a country has an S-shaped relationship. This indicates all countries can be divided into three stages accordingly. First, the poor co…
Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, it is often unclear which interventions to give, in what amounts, and for how long. Observational data in the form of electronic health recor…
Synthesizes computational approaches to understand neural timescales.
problem Varying definitions and measurements of neural timescales across studies.
method Reviews data analysis methods, biophysical models, and machine learning models.
result Complements experimental studies with a holistic view of neural timescales.
Agent learns diverse hierarchical structures in unknown environments.
problem Autonomous discovery and learning of diverse structures in unknown changing environments.
method Progressive construction of a Hierarchy of Observation Latent Models for Exploration Stratification (HOLMES).
result Agent can learn and reuse representations to progressively explore and discover diverse structures.
MGLM models all possible language channel factorizations for improved multilingual generation.
problem Generating multilingual text with flexibility and quality.
method Generative joint distribution model over language channels, marginalizing all possible factorizations.
result MGLM outperforms traditional models in multilingual generation tasks.
The paper sorts big data by revealed preferences, improving consumer and policy decisions.
problem Sorting diverse consumer preferences for big data objects like colleges.
method Endogenous weighting of revealed preferences, considering spillover effects.
result Consistent steady-state solution to counterbalance equilibrium.
Transformers learn to generalize out-of-distribution with diverse pretraining tasks.
problem Conditions for pretrained transformers to generalize out-of-distribution.
method Empirical study of task diversity and pretraining distribution.
result As task diversity increases, transformers transition from specialized to generalized solutions.
PyPOTS simplifies machine learning on time series with missing data.
problem Handling missing data in time series analysis.
method Unified interface for imputation, forecasting, anomaly detection, classification, and clustering.
result Robust and scalable Python toolkit for multivariate partially-observed time series.
CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.
problem Lack of alignment in neural recordings limits analysis of brain-wide dynamics.
method CREIMBO learns a unified model of neural dynamics by assuming multiple hidden global sub-circuits representing ensemble interactions.
result CREIMBO discovers session-specific neural ensembles and their non-stationary interactions, revealing cross-subject neural mechanisms.
We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression. AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incu…
Dynamics of observables in rank-based models and performance of functionally generated portfoliosmath.PR
In the seminal work [9], several macroscopic market observables have been introduced, in an attempt to find characteristics capturing the diversity of a financial market. Despite the crucial importance of such observables for investment decisions, a concise mathematical description of their dynamics has been missing. W…
DVERGE diversifies adversarial vulnerabilities to enhance robust ensemble models.
problem Diverse adversarial vulnerabilities for robust ensemble models.
method Isolates and diversifies adversarial vulnerabilities through distillation and training.
result Achieves higher robustness against transfer attacks compared to previous methods.
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc. over subsets of items. We focus on the setting where the universe of items is large (ranging from thousands to millio…
DiTSNe-Ia model accurately reconstructs supernovae spectra from light curves.
problem Difficult identification and interpretation of diverse sub-populations of supernovae.
method Variational diffusion-based generative model conditioned on light curves.
result DiTSNe-Ia achieves significantly more accurate reconstructions than SALT3 across all phases.
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios …
This paper introduces a new system for discovering patterns in morphogenetic systems using modular architecture and unsupervised learning.
problem Discovering novel patterns in morphogenetic systems is challenging and often relies on manual tuning.
method Introduces a hierarchical, modular architecture for unsupervised learning of diverse representations combined with goal exploration algorithms.
result The new system efficiently adapts diversity search towards user preferences with minimal feedback.
New algorithms learn POMDPs efficiently with hindsight observability.
problem Hardness of learning in POMDPs due to partial observability.
method Hindsight Observable Markov Decision Process (HOMDP) and new algorithms for tabular and function approximation settings.
result Sample-efficient learning in POMDPs with optimal dependence on latent state and observation cardinalities.
A new method clusters heterogeneous subgroups for accurate causal learning.
problem Diverse causal relationships across different time spans, regions, or strategies.
method Nonlinear Causal Kernel Clustering
result Reduction in prediction error through enhanced causal learning.
New method detects out-of-distribution samples in regression tasks.
problem Detecting instances far from training data in regression models.
method Estimating predictor entropy based on nearest neighbors and generative models.
result A new method for robust OOD detection in regression tasks.
New theory explains GAN's high quality but low diversity.
problem Lack of theoretical justification for non-saturating GAN training.
method Showed non-saturating GAN training approximately minimizes a specific f-divergence.
result Non-saturating GAN training minimizes a particular f-divergence.
GANs mode collapse solved with Bures distance.
problem GANs mode collapse or mode dropping.
method Use Bures distance to match real and fake batch diversity in feature space.
result Diversity matching reduces mode collapse and improves sample quality.
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has re…
We study --both in theory and practice-- the use of momentum motions in classic iterative hard thresholding (IHT) methods. By simply modifying plain IHT, we investigate its convergence behavior on convex optimization criteria with non-convex constraints, under standard assumptions. In diverse scenaria, we observe that …
The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and …
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger numb…
Theories of Accounting: Evolution & Developments, Income-Determination and Diversities in Useq-fin.GN
Accounting frameworks follow stipulations of existing Accounting Theories. This exploratory research sets out to trace the evolution of accounting theories of Charge and Discharge Syndrome and the Corollary of Double Entry. Furthermore, it dives into the theories of Income Determination, garnishing it with areas of div…
GAN+VER improves GANs by regularizing entropy to reduce mode collapse.
problem Mode collapse in GANs where the generator fails to capture all modes.
method Maximizing a variational lower bound on the entropy of generated samples.
result Significant improvement in evaluation metrics for real and generated samples.
A System for Morphology-Task Generalization via Unified Representation and Behavior Distillationcs.LG
A method for a single policy to solve various tasks across diverse agent morphologies.
problem Generalizing a single policy to solve various tasks across diverse agent morphologies.
method Unified representation and behavior distillation using a morphology-task graph and Transformer architecture.
result Improves multi-task performances compared to baselines, suggesting a promising approach.
Machine learning models predict which ideas will be innovated based on subjective perspectives.
problem Predicting high-impact innovation based on subjective perspectives and interpersonal innovation opportunities.
method Quantifying subjective perspectives and their interaction based on innovator positions within a geometric space of concepts.
result Subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future.
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
Improves sample efficiency and generalization in vision-based RL by enhancing exploration.
problem Low sample efficiency in vision-based RL using images as observations.
method Integrates state representation learning to enhance exploration and sample diversity.
result Significant improvement in sample efficiency across various environments.