prompt
stringlengths
41
511
target
stringlengths
1
25
keyword
stringclasses
697 values
full_sentence
stringlengths
48
1.25k
to bridge this gap we systematically evaluated eight
probabilistic
recurrent neural networks
to bridge this gap we systematically evaluated eight probabilistic deep learning models including two foundation models that have demonstrated strong performance on general forecasting benchmarks
large language models llms commonly boost reasoning via sample-evaluate-ensemble decoders achieving
label
language models
large language models llms commonly boost reasoning via sample-evaluate-ensemble decoders achieving label free gains without ground truth
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
artificial neural
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
recursive feasibility and finite-time convergence are ensured through an adaptive terminal
constraint
soft constraints
recursive feasibility and finite-time convergence are ensured through an adaptive terminal constraint mechanism
the good agreement with experimental estimates demonstrates how time-independent variational calculations of excited states using
density
density functional
the good agreement with experimental estimates demonstrates how time-independent variational calculations of excited states using density functionals can give accurate results and thereby provide a powerful screening tool for identifying other defect systems as candidates for quantum technologies
beating the winner s curse via inference-aware
policy
policy evaluation
beating the winner s curse via inference-aware policy optimization
while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography
eeg
functional connectivity
while resting-state fmri studies have revealed large-scale network correlates of creative potential electroencephalography eeg offers a temporally precise and scalable approach to capture the fast oscillatory dynamics that underlie spontaneous neural organization
the agent adapts message specificity and timing to the particulars of a user and scenario based on projections of how prior-guided interpretation of messages will influence attention to the interface and subsequent
belief
temporal semantics
the agent adapts message specificity and timing to the particulars of a user and scenario based on projections of how prior-guided interpretation of messages will influence attention to the interface and subsequent belief update across several timesteps out to a fixed horizon
instead of sharing a global low-resolution space each part in our method - even small ones - is
generated
layout generation
instead of sharing a global low-resolution space each part in our method - even small ones - is generated at full resolution enabling the synthesis of intricate details
we propose to combine the reconstruction loss with training for dynamic correspondence along with a visibility head and fine-tuning mast3r for point
tracking
point tracking
we propose to combine the reconstruction loss with training for dynamic correspondence along with a visibility head and fine-tuning mast3r for point tracking using a relatively small amount of synthetic data
this study bridges the gap between the two approaches by showing that both are based on essentially the same
optimization
minimax optimal
this study bridges the gap between the two approaches by showing that both are based on essentially the same optimization problem
we systematically study the spectral-norm error tilde a -1 _p - a_p -1 for an n times n symmetric
matrix
spectral density matrices
we systematically study the spectral-norm error tilde a -1 _p - a_p -1 for an n times n symmetric matrix a where a_p -1 denotes the best rank- p approximation of a -1 and tilde a a e is a noisy observation
species distribution models sdms which aim to predict
species
ecological interactions
species distribution models sdms which aim to predict species occurrence based on environmental variables are widely used to monitor and respond to biodiversity change
with those we perform a systematic analysis of the group s dynamics in the core and its embedding in the local
cosmic
dark matter
with those we perform a systematic analysis of the group s dynamics in the core and its embedding in the local cosmic environment
our model uniquely utilizes a quantum-enhanced generator that outputs parameters mean and log-variance of a gaussian distribution via reparameterization
combined
generative ai
our model uniquely utilizes a quantum-enhanced generator that outputs parameters mean and log-variance of a gaussian distribution via reparameterization combined with a wasserstein critic to stabilize adversarial training
importantly our method operates on probability
predictions
predictive performance
importantly our method operates on probability predictions and event outcomes and does not require under-the-hood access to the machine learning model
we introduce a logic called neighborhood operator logic with acyclicity connectivity and clique constraints mathsf neo _2 mathsf ack for short that captures all np-hard problems unicode x2013 like independent set or hamiltonian cycle unicode x2013 that are known to be tractable in time 2 mathcal o k n mathcal o 1 and space n mathcal o 1 on n -vertex
graphs
regular graphs
we introduce a logic called neighborhood operator logic with acyclicity connectivity and clique constraints mathsf neo _2 mathsf ack for short that captures all np-hard problems unicode x2013 like independent set or hamiltonian cycle unicode x2013 that are known to be tractable in time 2 mathcal o k n mathcal o 1 and space n mathcal o 1 on n -vertex graphs provided with elimination forests of depth k
the model naturally accepts interleaved vision-language inputs and generates interleaved
vision-language
vision-language models vlms
the model naturally accepts interleaved vision-language inputs and generates interleaved vision-language outputs
they do not break network nonlocality which enables one to identify useful quantum
channels
quantum channels
they do not break network nonlocality which enables one to identify useful quantum channels in networks
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the
prior
data-driven stabilization
to formalize this we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the prior knowledge
in this paper we prove that a graph is of ferrer
dimension
ferrers dimension
in this paper we prove that a graph is of ferrer dimension three equivalent to the intersection bigraph of orthants and points in mathbb r 3 if and only if it admits a biadjacency matrix representation that does not contain gamma begin bmatrix 1 1 0 1 0 1 end bmatrix and delta begin bmatrix 1 0 1 1 0 1 end bmatrix where denotes zero or one entry
numerous mitigation methods exist for quantum noise suppression making it challenging to identify the optimum approach for a specific application especially as ongoing advances in hardware tuning and error correction are expected to reduce logical
error
quantum computing
numerous mitigation methods exist for quantum noise suppression making it challenging to identify the optimum approach for a specific application especially as ongoing advances in hardware tuning and error correction are expected to reduce logical error rates
tests on synthetic and real-world networks show that the genepy can shed new light about the nodes centrality carrying information generally poorly correlated with the nodes number of direct connections
nodes
correlation network
tests on synthetic and real-world networks show that the genepy can shed new light about the nodes centrality carrying information generally poorly correlated with the nodes number of direct connections nodes degree
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement
channels
quantum coherence
finally we present a proof-of-principle simulation on the ibm quantum experience platform realizing a quantum switch of two measurement channels with tunable strengths and experimentally confirming the predicted efficiency enhancement enabled by correlation-assisted superposed causal order
existing approaches often overlook these spatial relationships limiting their
flexibility
existing approaches
existing approaches often overlook these spatial relationships limiting their flexibility and scalability
we demonstrate that af consistently outperforms rf xgboost and other weighted rf in binary and multi-class classification problems over 20
real-world
real-world datasets
we demonstrate that af consistently outperforms rf xgboost and other weighted rf in binary and multi-class classification problems over 20 real-world datasets
the realization of this vision critically depends on developing advanced receiver architectures that merge nanoscale
communication
wireless communication
the realization of this vision critically depends on developing advanced receiver architectures that merge nanoscale communication and networking techniques with bio-cyber interfaces ensuring energy-efficient reliable and low-complexity modulation and detection while maintaining biocompatibility
the authors address the challenge posed by rapidly evolving sub-cultural
languages
multilingual data
the authors address the challenge posed by rapidly evolving sub-cultural languages and slang which complicate automated information extraction and law enforcement monitoring
taken together our analysis clarifies the trade-off between
action
vision-language models vlms
taken together our analysis clarifies the trade-off between action fine-tuning and the degradation of vl representations and highlights practical approaches to recover inherited vl capabilities
importantly our method operates on probability
predictions
debiased machine learning
importantly our method operates on probability predictions and event outcomes and does not require under-the-hood access to the machine learning model
these findings suggest that reinforcement learning approaches can be effectively adapted for use in random
nonstationary
continual learning
these findings suggest that reinforcement learning approaches can be effectively adapted for use in random nonstationary and reward-sparse environments
monte carlo simulations show that kp usually performs better than j which is prone to severe
size
monte carlo
monte carlo simulations show that kp usually performs better than j which is prone to severe size distortions
however no consistent relationships were found between bird
communities
ecological communities
however no consistent relationships were found between bird communities and abiotic variables such as weather temperature and elevation likely due to the single-visit sampling design
maps converts attribution maps into explanation-masked images emis and compares image-by-image human accuracies on these minimal
images
receptive fields
maps converts attribution maps into explanation-masked images emis and compares image-by-image human accuracies on these minimal images with limited pixel budgets with accuracies on the full stimuli
a research roadmap for augmenting software engineering processes and software products with
generative
generative models
a research roadmap for augmenting software engineering processes and software products with generative ai
this paper studies how to achieve accurate
modeling
data-driven stabilization
this paper studies how to achieve accurate modeling and effective control in stochastic nonlinear dynamics with multiple interacting objects
we envision a new era of ai termed agentic organization where
agents
language agents
we envision a new era of ai termed agentic organization where agents solve complex problems by working collaboratively and concurrently enabling outcomes beyond individual intelligence
we also show that the method naturally extends to graph
states
quantum dot
we also show that the method naturally extends to graph states that are local clifford equivalent to ghz states
we unveil the behaviour of the system for all
physical
complex systems
we unveil the behaviour of the system for all physical relevant values of the parameters and several representative interaction networks
diffusion models for wireless transceivers from pilot-efficient
channel
wireless systems
diffusion models for wireless transceivers from pilot-efficient channel estimation to ai-native 6g receivers
it further decomposes the evaluation of llm performance into six fundamental
capabilities
llm agents
it further decomposes the evaluation of llm performance into six fundamental capabilities including opinion consistency memory recall logical reasoning lexical fidelity persona tone and syntactic style
to address the practical demands of multi-modal open-vocabulary goal queries and multi-goal visual navigation we propose lagmemo a
navigation
visual navigation
to address the practical demands of multi-modal open-vocabulary goal queries and multi-goal visual navigation we propose lagmemo a navigation system that leverages a language 3d gaussian splatting memory
our spg-cdenet consists of two key components a spatial prior network and a cross dual
encoder
encoder network
our spg-cdenet consists of two key components a spatial prior network and a cross dual encoder network
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar
masses
star clusters
the simulated galaxies reproduce the observed stellar-to-halo mass mass--metallicity and size--mass relations yielding stellar masses of 10 6-10 8 m_ odot and metallicities consistent with those of local group dwarf galaxies
yet it remains unclear whether constrained prompts
actually
user experience
yet it remains unclear whether constrained prompts actually improve player experience
nonetheless this approach typically assumes a deterministic payoff
structure
collective systems
nonetheless this approach typically assumes a deterministic payoff structure for social interactions
to address these limitations we propose ordermind a unified spatial-aware manipulation ordering framework that directly learns object
manipulation
manipulation ordering
to address these limitations we propose ordermind a unified spatial-aware manipulation ordering framework that directly learns object manipulation priorities based on spatial context
i show that under independence assumptions vars can identify average treatment effects
average
policy evaluation
i show that under independence assumptions vars can identify average treatment effects average causal responses or a mix of the two depending on the distribution of the policy
for pcg and ecg signals recorded from the device in noisy hospital settings the proposed algorithms achieved signal-to-noise
ratio
signal-to-noise ratio
for pcg and ecg signals recorded from the device in noisy hospital settings the proposed algorithms achieved signal-to-noise ratio improvements of 37
we refer to this riesz representer estimation as generalized
riesz
riesz regression
we refer to this riesz representer estimation as generalized riesz regression
our results thus demonstrate a robust route toward scalable and
efficient
quantum computing
our results thus demonstrate a robust route toward scalable and efficient boltzmann sampling on current quantum processors
this enables dynamic and real-time tasks that were previously
believed
real-world applications
this enables dynamic and real-time tasks that were previously believed to be unattainable by large vla models
network nonlocality a recently noted form of nonlocality has been shown to have distinctive features marking a significant departure from the notion of standard bell
nonlocality
quantum channels
network nonlocality a recently noted form of nonlocality has been shown to have distinctive features marking a significant departure from the notion of standard bell nonlocality in the context of quantum correlations
third traditional difference-in-differences methods that ignore
spatial
treatment effect
third traditional difference-in-differences methods that ignore spatial and network structure exhibit 61 percent bias in estimated treatment effects
genesis thus advances a unified theoretical framework that bridges semantic and
episodic
working memory
genesis thus advances a unified theoretical framework that bridges semantic and episodic memory offering new insights into the generative foundations of human cognition
our study focuses on how assembly history governs the structural and kinematic
diversity
galaxy cgm
our study focuses on how assembly history governs the structural and kinematic diversity of dwarf galaxies within the lambda cdm framework
network nonlocality a recently noted form of nonlocality has been shown to have distinctive features marking a significant departure from the notion of standard bell
nonlocality
quantum correlations
network nonlocality a recently noted form of nonlocality has been shown to have distinctive features marking a significant departure from the notion of standard bell nonlocality in the context of quantum correlations
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large
language
language models
existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models llms
the accuracy and reliability of this methodology is further demonstrated through a series of
numerical
numerical experiments
the accuracy and reliability of this methodology is further demonstrated through a series of numerical experiments
existing theoretical frameworks typically assume homogeneous network structures and constant or pulse-like inputs overlooking how heterogeneity in structure and variety of input shape
transient
transient dynamics
existing theoretical frameworks typically assume homogeneous network structures and constant or pulse-like inputs overlooking how heterogeneity in structure and variety of input shape transient responses often leading to discrepancies between theory and observation
communication and verification in llm agents towards
collaboration
llm agents
communication and verification in llm agents towards collaboration under information asymmetry
the drl agent interacts with the environment and learns optimal strategies through trial and error
guided
deep reinforcement
the drl agent interacts with the environment and learns optimal strategies through trial and error guided by predefined rewards
we collected a larger and more diverse dataset that includes both
road
autonomous driving
we collected a larger and more diverse dataset that includes both road and grass terrains and validated seq-deepipc on a robot dog
however brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features still posing challenges for decoding
fmri
receptive fields
however brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features still posing challenges for decoding fmri representations under the supervision of images
our models allow us to fit continuum shapes bounded together with the line profiles which gives additional constraints on the gas structure of
wa
dense gas
our models allow us to fit continuum shapes bounded together with the line profiles which gives additional constraints on the gas structure of wa in this source
our findings provide actionable insights into how values are learned during post-training and help to inform data curation as well as the selection of models and algorithms for
preference
preference optimization
our findings provide actionable insights into how values are learned during post-training and help to inform data curation as well as the selection of models and algorithms for preference optimization to improve model alignment to human values
yet when applied to train multi-turn interactive
llm
llm agents
yet when applied to train multi-turn interactive llm agents these methods often suffer from structural blindness-the inability to exploit the underlying connectivity of the environment
the theory conceptualizes social systems as ensembles of social atoms capable of absorbing and emitting quantized units of
social
emergent behaviors
the theory conceptualizes social systems as ensembles of social atoms capable of absorbing and emitting quantized units of social energy
this result shows that even under relaxed assumptions quantum
theory
quantum mechanics
this result shows that even under relaxed assumptions quantum theory resists reconciliation with classical notions of absolute events reinforcing the foundational significance of wigner s friend-type paradoxes in timelike scenarios
we propose a data-driven framework for efficiently solving quadratic
programming
efficiently solving
we propose a data-driven framework for efficiently solving quadratic programming qp problems by reducing the number of variables in high-dimensional qps using instance-specific projection
this entanglement biases conventional brain encoding analyses toward
linguistically
inner speech
this entanglement biases conventional brain encoding analyses toward linguistically shallow features e
plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of
first-stage
regression function
plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of first-stage bias
we consider a class of algorithms for this
problem
learning algorithm
we consider a class of algorithms for this problem which is provably minimax optimal up to a constant factor
the nonlinear disturbance observer attenuates constant and nonlinear
disturbances
bounded disturbances
the nonlinear disturbance observer attenuates constant and nonlinear disturbances as well as band-limited white noise
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative
reasoning
large language
while large language models llms have demonstrated remarkable performance across various reasoning tasks their ability to handle normative reasoning remains underexplored
a unified theory for causal inference direct debiased machine
learning
causal effects
a unified theory for causal inference direct debiased machine learning via bregman-riesz regression
in network-based sis models of infectious disease transmission
infection
infectious individuals
in network-based sis models of infectious disease transmission infection can only occur between directly connected individuals
by fitting up to three three epochs per source with a fully
relativistic
black hole
by fitting up to three three epochs per source with a fully relativistic disk model we show that many system properties can be reliably recovered including importantly the black hole mass m_ bullet
we demonstrate this method in simulation and discuss how textit a priori understandings of
obstacle
obstacle avoidance
we demonstrate this method in simulation and discuss how textit a priori understandings of obstacle risk can be directly incorporated into the safety filter to generate safe behaviors that are risk-aware
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual
encoder
encoder network
the prior network generates coarse localization maps that delineate the approximate roi serving as spatial guidance for the dual encoder network
omnilayout enabling coarse-to-fine learning with llms for universal document
layout
layout generation
omnilayout enabling coarse-to-fine learning with llms for universal document layout generation
we note that all the main predictions on galaxy quenched fractions and smbh growth
histories
massive stars
we note that all the main predictions on galaxy quenched fractions and smbh growth histories and scaling relations are degenerate with those expected in a halo quenching model
this paper addresses the challenges of giving a causal interpretation to
vector
vector autoregression
this paper addresses the challenges of giving a causal interpretation to vector autoregressions vars
we demonstrate the performance of our algorithms in numerical simulations where they are used as an integral part of several iterative image reconstruction techniques classic variational methods such as non-negative least squares and total variation regularized least squares as well as
deep
gradient descent
we demonstrate the performance of our algorithms in numerical simulations where they are used as an integral part of several iterative image reconstruction techniques classic variational methods such as non-negative least squares and total variation regularized least squares as well as deep learning methods such as learned primal dual
this capability is particularly relevant for active photonic circuits that generate
quantum
quantum emitters
this capability is particularly relevant for active photonic circuits that generate quantum light directly on-chip
in this paper we systematically evaluate llms
reasoning
models llms
in this paper we systematically evaluate llms reasoning capabilities in the normative domain from both logical and modal perspectives
adapting large language models llms via reinforcement learning rl is often bottlenecked by the generation stage which can consume over 75 of the
training
continual learning
adapting large language models llms via reinforcement learning rl is often bottlenecked by the generation stage which can consume over 75 of the training time
future work should reevaluate existing findings using the local gaussian
correlation
local gaussian correlation
future work should reevaluate existing findings using the local gaussian correlation method
results demonstrate effective trajectory tracking and real-time state
estimation
state estimation
results demonstrate effective trajectory tracking and real-time state estimation highlighting the platform s potential as a cost effective and versatile tool for advanced research and educational applications
through our work we aim to foster state of the art research into generalizable robust and safe end-to-end autonomous
driving
autonomous driving
through our work we aim to foster state of the art research into generalizable robust and safe end-to-end autonomous driving agents capable of handling complex real-world situations
linearly transforming stimulus representations of deep neural networks yields high-performing models of
behavioral
receptive fields
linearly transforming stimulus representations of deep neural networks yields high-performing models of behavioral and neural responses to complex stimuli
object-context shortcuts remain a persistent challenge in
vision-language
vision-language models vlms
object-context shortcuts remain a persistent challenge in vision-language models undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences
in many problems involving causal effects or structural models the parameters of interest depend on
regression
causal effect
in many problems involving causal effects or structural models the parameters of interest depend on regression functions
using network gradients it is possible to identify regions where the network pays attention during image
recognition
computer vision
using network gradients it is possible to identify regions where the network pays attention during image recognition tasks
here we present a systematic analysis of four
quasars
active galactic
here we present a systematic analysis of four quasars initially selected by their ks-band variability amplitudes in the vista variables in the v i a l actea survey vvv vvvx
to address this issue we propose a novel obstacle avoidance algorithm cooperative non-inertial frame-based obstacle avoidance coni-oa designed explicitly for uav-ugv cooperative scenarios without reliance on global state estimation or
obstacle
dynamic obstacles
to address this issue we propose a novel obstacle avoidance algorithm cooperative non-inertial frame-based obstacle avoidance coni-oa designed explicitly for uav-ugv cooperative scenarios without reliance on global state estimation or obstacle prediction
for this correlation based photonic quantum computer the channels of the wave guides represent basis
states
quantum technologies
for this correlation based photonic quantum computer the channels of the wave guides represent basis states of a multi-qubit system rather than individual qubits
we benchmark against a random sampling approach and we find that our optimization-based approach always finds larger
load
load shedding
we benchmark against a random sampling approach and we find that our optimization-based approach always finds larger load shedding
to tackle this challenge we propose braincognizer a novel brain decoding model
inspired
neural representations
to tackle this challenge we propose braincognizer a novel brain decoding model inspired by human visual cognition which explores multi-level semantics and correlations without fine-tuning of generative models
our results highlight the nuanced social dynamics of ai-mediated authorship and inform design implications for creating transparent context-sensitive writing
systems
ai literacy
our results highlight the nuanced social dynamics of ai-mediated authorship and inform design implications for creating transparent context-sensitive writing systems that better preserve trust and authenticity