eamag/beyond_benchmarks · Datasets at Hugging Face

specifically by leveraging cutting-edge majorization-minimization mm and penalty-dual-decomposition pdd methods we develop a low-complexity algorithm to solve the beamformer configuration

problem

beamforming design

specifically by leveraging cutting-edge majorization-minimization mm and penalty-dual-decomposition pdd methods we develop a low-complexity algorithm to solve the beamformer configuration problem containing the fractional and quartic terms

deep learning dl techniques offer promising alternatives however they are often constrained by challenges such as data scarcity and high-dimensional

output

deep network

deep learning dl techniques offer promising alternatives however they are often constrained by challenges such as data scarcity and high-dimensional output requirements

we empirically verify our theoretical findings on a variety of

imbalanced

imbalanced data

we empirically verify our theoretical findings on a variety of imbalanced datasets

we jointly optimize all parameters including the transmitter precoder receiver combiner and ris phase shifts under practical constraints such as transmit

power

transmit power

we jointly optimize all parameters including the transmitter precoder receiver combiner and ris phase shifts under practical constraints such as transmit power budget and unit-modulus phase shift requirements

in this work we report momentum-resolved reflectivity measurements on

photonic

photonic crystal

in this work we report momentum-resolved reflectivity measurements on photonic crystals that are periodic in two dimensions and homogeneous over a thickness of 5 mu m

interstellar comet 3i atlas evidence for galactic cosmic

ray

interstellar medium

interstellar comet 3i atlas evidence for galactic cosmic ray processing

the analysis is complemented by vector autoregression impulse responses and forecast error variance decompositions see granger 1969 sims 1980 heterogeneous

autoregressive

vector autoregression

the analysis is complemented by vector autoregression impulse responses and forecast error variance decompositions see granger 1969 sims 1980 heterogeneous autoregressive models with exogenous regressors har-x corsi 2009 and a leakage-safe machine learning protocol using temporal splits early stopping validation-only thresholding and shap-based interpretation

as a crucial technical contribution we derive all our

asymptotics

asymptotic normality

as a crucial technical contribution we derive all our asymptotics under the assumption that the kernels associated with our u-statistics are square summable instead of requiring the typical absolute summability which makes our assumption naturally easier to check

2 percentage points pp higher micro-f1 than llm-only and rag baselines with fewer under- and over-predictions resulting in

higher

findings highlight

2 percentage points pp higher micro-f1 than llm-only and rag baselines with fewer under- and over-predictions resulting in higher recall and lower false positive rates

to address these challenges we introduce a new class of priors for bnns called mercer priors such that the resulting bnn has samples which

approximate

approximate posterior

to address these challenges we introduce a new class of priors for bnns called mercer priors such that the resulting bnn has samples which approximate that of a specified gp

multi-task learning based on support vector machines and twin

support

support vector

multi-task learning based on support vector machines and twin support vector machines a comprehensive survey

using a simulation model based on the prisoner s dilemma and quantal response equilibrium we analyze how variations in these learning rates affect the

emergence

emergent behaviors

using a simulation model based on the prisoner s dilemma and quantal response equilibrium we analyze how variations in these learning rates affect the emergence of large-scale network structures

we find that dwarf agn selected by infrared colors are the most distinct population with the highest star formation

rates

star-forming region

we find that dwarf agn selected by infrared colors are the most distinct population with the highest star formation rates and lowest stellar masses

identification and semiparametric estimation of

conditional

nonparametric identification

identification and semiparametric estimation of conditional means from aggregate data

leveraging this equivalence we propose a novel regularization method for

policy

policy learning

leveraging this equivalence we propose a novel regularization method for policy learning

waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying

photonic

integrated photonics

waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying photonic qubits in quantum information applications

examples are given to confirm the tightness of

competitive

competitive ratio

examples are given to confirm the tightness of competitive analysis

however these evaluations rely on llms proxy

llms

llm reasoning

however these evaluations rely on llms proxy llms to gauge compliance with privacy norms overlooking real users perceptions

including lifecycle emissions further raises renewable

deployment

carbon emissions

including lifecycle emissions further raises renewable deployment by 25

as applications we apply the method to ground-state preparation of the transverse-field ising cluster-ising and fermi-hubbard

models

quantum error correction

as applications we apply the method to ground-state preparation of the transverse-field ising cluster-ising and fermi-hubbard models achieving consistently higher accuracy under the same gate budget compared with standard vqas

existing infrared and visible image fusion methods often face the dilemma of balancing

modal

image fusion

existing infrared and visible image fusion methods often face the dilemma of balancing modal information

such a method features a notable advantage it allows units to be sparsely

treated

treatment effect

such a method features a notable advantage it allows units to be sparsely treated capturing the impact of interventions on the innovation component of the outcome variables

inspired by predictive coding in neuroscience--which suggests that the brain predicts sensory inputs as a neural implementation of bayesian inference--and by auxiliary predictive objectives in deep rl we investigate whether integrating self-supervised

predictive

predictive processing

inspired by predictive coding in neuroscience--which suggests that the brain predicts sensory inputs as a neural implementation of bayesian inference--and by auxiliary predictive objectives in deep rl we investigate whether integrating self-supervised predictive coding modules into meta-rl can facilitate learning of bayes-optimal representations

we used these disentangled embeddings to model intracranial ecog brain

recordings

brain decoding

we used these disentangled embeddings to model intracranial ecog brain recordings from neurosurgical patients listening to natural speech

this result underscores the necessity and effectiveness of

domain-specific

machine learning

this result underscores the necessity and effectiveness of domain-specific training

through both empirical investigation and theoretical analysis we reveal that gc is inherently an intrinsic-dimension-reducing process synthesizing a condensed

graph

graph neural

through both empirical investigation and theoretical analysis we reveal that gc is inherently an intrinsic-dimension-reducing process synthesizing a condensed graph with lower classification complexity

the proposed chebyshev ensemble geometric network ch-egn is a hybrid convolutional and geometric

deep

deep network

the proposed chebyshev ensemble geometric network ch-egn is a hybrid convolutional and geometric deep learning

waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying

photonic

photonic crystal

waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying photonic qubits in quantum information applications

we release code pretrained weights and tutorials to support standardized

eeg

brain-computer interface

we release code pretrained weights and tutorials to support standardized eeg research and accelerate progress in clinical neuroscience

the performance of large language models llms often degrades when crucial information is in the middle of a long context a lost-in-the-middle phenomenon that mirrors the

primacy

memory demand

the performance of large language models llms often degrades when crucial information is in the middle of a long context a lost-in-the-middle phenomenon that mirrors the primacy and recency effects in human memory

these centi-combs bridge the gap between conventional mode-locked lasers and microresonator frequency

combs

frequency combs

these centi-combs bridge the gap between conventional mode-locked lasers and microresonator frequency combs providing a new route towards real-time sampling optical-to-microwave synchronization and hybrid optical clock networks in a compact form

to address these limitations the proposed system

introduces

physical virtual

to address these limitations the proposed system introduces a vr-based assistive platform integrating panoramic visuals and haptic feedback to create an immersive training environment

finally the shapes x-shaped boxy of bulge

stars

bulge stars

finally the shapes x-shaped boxy of bulge stars with different metallicities were analyzed through least-squares fitting based on the analytical bulge models

however as typical to any quantum resource network

nonlocality

quantum mechanics

however as typical to any quantum resource network nonlocality is also vulnerable to environmental noise which sometimes prove to be detrimental

the key task of machine learning is to minimize the loss function that measures the model fit to the

training

machine learning

the key task of machine learning is to minimize the loss function that measures the model fit to the training data

5 exhibits strong native multimodal capabilities including long-horizon

vision-language

vision-language models

5 exhibits strong native multimodal capabilities including long-horizon vision-language generation any-to-image x2i generation and complex text-rich image generation

our results demonstrate that llm-based simulation is a practical and easy-to-implement way to improve experimental

design

randomized experiments

our results demonstrate that llm-based simulation is a practical and easy-to-implement way to improve experimental design in covariate-rich settings

the cost function of the mhe optimization

problem

inverse optimal issf

the cost function of the mhe optimization problem is suitably designed to accommodate these irregular output sequences

this capability is particularly relevant for active

photonic

photonic crystal

this capability is particularly relevant for active photonic circuits that generate quantum light directly on-chip

locot2v-bench a benchmark for long-form and complex

text-to-video

image generation

locot2v-bench a benchmark for long-form and complex text-to-video generation

rethinking cross-lingual alignment balancing transfer and cultural erasure in

multilingual

large language

rethinking cross-lingual alignment balancing transfer and cultural erasure in multilingual llms

the classical davis-kahan theorem provides an efficient bound on the perturbation of eigenspaces of a

matrix

spectral density matrices

the classical davis-kahan theorem provides an efficient bound on the perturbation of eigenspaces of a matrix under a large eigenvalue gap condition

n of a text t of length n encodes the lexicographic order of its suffixes and underlies numerous applications in

pattern

pattern matching

n of a text t of length n encodes the lexicographic order of its suffixes and underlies numerous applications in pattern matching data compression and bioinformatics

we further formalize the generalization problem in meta-reinforcement learning and establish corresponding

generalization

reinforcement learning

we further formalize the generalization problem in meta-reinforcement learning and establish corresponding generalization bounds

pvmark enabling public verifiability for llm

watermarking

watermarking schemes

pvmark enabling public verifiability for llm watermarking schemes

we propose viz-coast a method of leveraging the common-sense spatial

reasoning

visual navigation

we propose viz-coast a method of leveraging the common-sense spatial reasoning of large pretrained vision-language models to identify issues with downward refinement a priori bypassing the need to fix these failures during planning

however these evaluations rely on llms proxy

llms

llm raters

however these evaluations rely on llms proxy llms to gauge compliance with privacy norms overlooking real users perceptions

we show that the truncated random return can be naturally expressed in the

quadratic

truncated random return

we show that the truncated random return can be naturally expressed in the quadratic form

as a new and promising approach existing machine

unlearning

continual learning

as a new and promising approach existing machine unlearning mu works typically emphasize theoretical formulations or optimization objectives to achieve knowledge removal

however these models often lack transparency and explainability can be costly to fine-tune require

substantial

smaller models

however these models often lack transparency and explainability can be costly to fine-tune require substantial prompt engineering yield inconsistent results across domains and impose significant adverse environmental impact due to their high computational demands

to this end we design a projection and soft-aggregation mechanism for

flow

flow matching

to this end we design a projection and soft-aggregation mechanism for flow inspired by gradient conflict resolution in multi-task learning

plugging regression functions estimated by machine learning

methods

machine learning

plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of first-stage bias

we show that minimizing the bayes cost mean of the cross-entropy loss implies maximizing the

mutual

mutual information

we show that minimizing the bayes cost mean of the cross-entropy loss implies maximizing the mutual information between the set of categories and the neural activities prior to the decision layer

quantifying emergent behaviors in agent-based

models

emergent behaviors

quantifying emergent behaviors in agent-based models using mean information gain

multi-task learning based on support vector machines and twin support

vector

support vector machines

multi-task learning based on support vector machines and twin support vector machines a comprehensive survey

understanding how the human brain progresses from

processing

human brain

understanding how the human brain progresses from processing simple linguistic inputs to performing high-level reasoning is a fundamental challenge in neuroscience

human feedback is critical for aligning ai

systems

human-ai interaction

human feedback is critical for aligning ai systems to human values

the advancements in automatic modulation classification amc have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless

communication

uplink communication

the advancements in automatic modulation classification amc have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless communication environments

this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical

complexity

quantum correlations

this approach demonstrates that robust self-testing and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security

these findings highlight challenges in achieving logical consistency in llms

normative

normative reasoning

these findings highlight challenges in achieving logical consistency in llms normative reasoning and provide insights for enhancing their reliability

within this system framework we formulate an optimization

problem

optimization problem

within this system framework we formulate an optimization problem for the purpose of maximizing the minimum rate of users for each cell via designing the transmit beamforming of the trtc subject to the power constraints of each trtc unit

observational studies developing causal machine learning ml models for the prediction of individualized treatment effects ites seldom conduct empirical evaluations to assess the

conditional

conditional exchangeability

observational studies developing causal machine learning ml models for the prediction of individualized treatment effects ites seldom conduct empirical evaluations to assess the conditional exchangeability assumption

our work also provides a nuanced and generalizable framework for analyzing

opinion

opinion dynamics

our work also provides a nuanced and generalizable framework for analyzing opinion dynamics in other polarized public discourse

this system contains the system of coalescing random walks on the ancestral recombination graph as a special case and it sheds new light on the site-frequency spectrum sfs of

genetic

population genetics

this system contains the system of coalescing random walks on the ancestral recombination graph as a special case and it sheds new light on the site-frequency spectrum sfs of genetic data by specifying how sfs depends on the pedigree

we further examine the dependence on morphology and environment finding that s0 and early-type

spiral

milky way

we further examine the dependence on morphology and environment finding that s0 and early-type spiral galaxies exhibit stronger alignment signals than late-type spirals

we present sia social insight agents an llm

agent

llm agents

we present sia social insight agents an llm agent system that links heterogeneous multi-modal data -- including raw inputs e

evaluation results on a network with 18 intersections have demonstrated that by deploying uav detection at specific locations the

uncertainty

optimal uav

evaluation results on a network with 18 intersections have demonstrated that by deploying uav detection at specific locations the uncertainty reduction of macro-micro traffic state estimation ranges from 15

yet existing work has mostly contrasted the

behavior

emergent behaviors

yet existing work has mostly contrasted the behavior of a single agent with that of a collective of fixed size leaving open a central question how does group size shape dynamics

let x_i d_i y_i _ i 1 n be the observations where x_i in mathbb r p denotes p -dimensional

covariates

covariate balancing

let x_i d_i y_i _ i 1 n be the observations where x_i in mathbb r p denotes p -dimensional covariates d_i in 0 1 denotes a binary treatment assignment indicator and y_i in mathbb r is an outcome

we propose several setups for hierarchical control with options and derive practical algorithms following state-of-the-art

reinforcement

deep reinforcement

we propose several setups for hierarchical control with options and derive practical algorithms following state-of-the-art reinforcement learning techniques

to this end we introduce a new dataset covering a wide range of formal patterns of reasoning in both

normative

reasoning curriculum

to this end we introduce a new dataset covering a wide range of formal patterns of reasoning in both normative and epistemic domains while also incorporating non-formal cognitive factors that influence human reasoning

our results reinforce that quantum illumination is advantageous for spoofing resilience compared to a

classical

quantum technologies

our results reinforce that quantum illumination is advantageous for spoofing resilience compared to a classical illumination-based protocol

prior work establishes theoretical guarantees for langevin monte carlo algorithm based on overdamped and underdamped

langevin

langevin dynamics

prior work establishes theoretical guarantees for langevin monte carlo algorithm based on overdamped and underdamped langevin dynamics and more recently some third-order variants

formulas that determine the least favorable spectral densities and the minimax

spectral

spectral density

formulas that determine the least favorable spectral densities and the minimax spectral characteristics are proposed for some specific sets of admissible densities

theoretical methods for computing the band

structure

nonlinear optical

theoretical methods for computing the band structure in 2d are well-established and fast because 2d photonic crystals are homogeneous in the third dimension

the properties of stars and planets are shaped by the initial

conditions

star-forming region

the properties of stars and planets are shaped by the initial conditions of their natal clouds

this work introduces a dynamic context-aware scene reasoning framework that leverages

vision-language

vision-language models

this work introduces a dynamic context-aware scene reasoning framework that leverages vision-language alignment to address zero-shot real-world scenarios

this work represents the theoretical foundations of this cooperative manipulation control framework and thus the

experiments

optimal control

this work represents the theoretical foundations of this cooperative manipulation control framework and thus the experiments are presented in an abstract way while giving pointers towards potential future applications

first an equivalence between klyachko-can-binicio u g lu-shumovsky kcbs vectors and the pyramid upb is shown and then by constructing a one parameter family of upb vectors a quantitative connection between contextuality strength and bound entanglement of

states

quantum correlations

first an equivalence between klyachko-can-binicio u g lu-shumovsky kcbs vectors and the pyramid upb is shown and then by constructing a one parameter family of upb vectors a quantitative connection between contextuality strength and bound entanglement of states associated with the corresponding upb is demonstrated

all you need for object detection from pixels points and prompts to next-gen

fusion

object detection

all you need for object detection from pixels points and prompts to next-gen fusion and multimodal llms vlms in autonomous vehicles

our results include that finding a solution is fixed-parameter tractable with respect to the vertex cover number of the

food

food web

our results include that finding a solution is fixed-parameter tractable with respect to the vertex cover number of the food web assuming the phylogenetic tree is a star

simulation results demonstrate that the admm-apg algorithm consistently surpasses existing benchmark methods in terms of spectral

efficiency

spectral efficiency

simulation results demonstrate that the admm-apg algorithm consistently surpasses existing benchmark methods in terms of spectral efficiency and computational complexity achieving significant performance gains across a range of system configurations

reasoning path divergence a new metric and curation strategy to unlock llm

diverse

llm inference

reasoning path divergence a new metric and curation strategy to unlock llm diverse thinking

trajectory planning in dense interactive traffic scenarios presents significant challenges for autonomous vehicles primarily due to the uncertainty of human

driver

collision avoidance

trajectory planning in dense interactive traffic scenarios presents significant challenges for autonomous vehicles primarily due to the uncertainty of human driver behavior and the non-convex nature of collision avoidance constraints

in the absence of disturbances we find that standard inverse optimal safe

controllers

predictive control

in the absence of disturbances we find that standard inverse optimal safe controllers have a certain degree of gain margin

recent research on time series foundation models has primarily focused on

forecasting

time series classification

recent research on time series foundation models has primarily focused on forecasting leaving it unclear how generalizable their learned representations are

this paper presents a carbon-aware optimal power flow opf framework that incorporates data-driven carbon tracing enabling rapid estimation of nodal

carbon

carbon emissions

this paper presents a carbon-aware optimal power flow opf framework that incorporates data-driven carbon tracing enabling rapid estimation of nodal carbon emissions from electric loads

figuring out gas galaxies in enzo foggie xi

circumgalactic

circumgalactic medium

figuring out gas galaxies in enzo foggie xi circumgalactic o vi emission traces clumpy inflowing recycled gas

the proposed approach integrates pre-trained vision transformers and large language models to align visual semantics with natural

language

vision-language models vlms

the proposed approach integrates pre-trained vision transformers and large language models to align visual semantics with natural language descriptions enhancing contextual comprehension

to reduce such bias debiased machine learning employs

neyman

debiased machine

to reduce such bias debiased machine learning employs neyman orthogonal estimating equations

kernel logistic regression klr is a powerful

classification

machine learning

kernel logistic regression klr is a powerful classification method widely applied across diverse domains

but this method often fails to converge due to the system s high

dimensionality

linear convergence

but this method often fails to converge due to the system s high dimensionality and nonlinearity

we develop an efficient algorithm to solve this

bilevel

bilevel optimization

we develop an efficient algorithm to solve this bilevel optimization problem which computes parameter gradients without backpropagating through the solver

we suggest that interactions between parallel subnetworks in the brain may underlie such learning we present a model of representation learning by ensembles of neural networks where each network learns to encode stimuli into an

abstract

neural networks

we suggest that interactions between parallel subnetworks in the brain may underlie such learning we present a model of representation learning by ensembles of neural networks where each network learns to encode stimuli into an abstract representation space by cross-supervising interactions with other networks for inputs they receive simultaneously or in close temporal proximity

together these results suggest that nearly all correlations are not needed to predict neural activity and we provide the tools to uncover the key

correlations

functional connectivity

together these results suggest that nearly all correlations are not needed to predict neural activity and we provide the tools to uncover the key correlations that are

based on the similarity transformations of these cooperative geometric primitives we derive an abstraction of complex robotic systems that enables representing these

systems

multi-robot collaboration

based on the similarity transformations of these cooperative geometric primitives we derive an abstraction of complex robotic systems that enables representing these systems in a way that directly corresponds to single-arm systems

we present empathic prompting a novel framework for

multimodal

empathic prompting

we present empathic prompting a novel framework for multimodal human-ai interaction that enriches large language model llm conversations with implicit non-verbal context

in this paper we propose a deep reinforcement learning drl -based approach for dynamic beamforming and

power

power allocation

in this paper we propose a deep reinforcement learning drl -based approach for dynamic beamforming and power allocation in isac systems

to address this gap we curate omnilayout-1m the first million-scale dataset of diverse document layouts covering six common

document

layout generation

to address this gap we curate omnilayout-1m the first million-scale dataset of diverse document layouts covering six common document types and comprising contemporary layouts collected from multiple sources

additionally we used a gas-grain chemical code to simulate a pre-stellar

core

star-forming region

additionally we used a gas-grain chemical code to simulate a pre-stellar core and determine where ne can affect the chemistry