Tensor models decode human perception and memory using SPO triples.
problem Understanding implicit and explicit perception and memory in the brain.
method Tensor models with SPO triples, dual representations, and four layers.
result Semantic memory is crucial for explicit perception and declarative memories.
Bayesian models explain human time perception biases.
problem Understanding human time perception using Bayesian inference.
method Agent-based machine learning models and empirical data analysis.
result Bayesian models can replicate human time estimation biases.
A wearable EEG headband detects primary colors from brain activity for color perception.
problem Detecting primary colors from brain activity for color perception.
method Spectral power features, statistical features, and correlation features from continuous Morlet wavelet transform; dimensionality reduction techniques like Forward Feature Selection and Stacked Autoencoders; Random Forest Classifier.
result Best overall accuracy of 80.6% for intra-subject classification.
Global Planar Convolution boosts brain tumor segmentation by enhancing context perception.
problem Improving context perception in brain tumor segmentation networks.
method Introduced Global Planar Convolution module to enhance context aggregation in brain tumor segmentation.
result Global Planar Convolution eliminates the need for multiple representation levels in segmentation networks.
A new method for combining shapes in log-radius profiles.
problem Combining shape features accurately and intuitively.
method Log-radius profile representation and a new operation for curve combination.
result Accurately combines shape features in a visually intuitive manner.
New method shows stable phase synchronised patterns in EEG signals during face perception tasks.
problem Traditional phase synchronisation measures do not capture temporal evolution.
method Proposes a new method to identify synchrostates, small sets of unique phase synchronised patterns.
result Consistent existence of synchrostates in multi-channel EEG recordings across different subject groups.
New test assesses shared brain activity across different cognitive modalities.
problem Determining if different cognitive modalities use overlapping neural representations.
method Formulated a statistical hypothesis testing approach using permutation testing.
result New test (CMPT) has greater statistical power than cross-modal decoding while maintaining low Type I errors.
Study shows how brain completes missing parts of curves and constructs surfaces of negative curvature.
problem How the brain completes missing parts of curves and constructs surfaces of negative curvature.
method Solving variational problems to find sub-Riemannian geodesics and constructing surfaces of constant negative curvature.
result There is a one-to-one correspondence between sub-Riemannian geodesics used by the brain and rotational surfaces of constant negative curvature.
Deep learning enhances active inference for dynamic state spaces.
problem Limited applicability of active inference to continuous state spaces.
method Use of deep learning to approximate probability distributions for active inference.
result Active inference can be applied to continuous state spaces.
The paper explores how organisms and machines learn from their environment using machine learning, information theory, and thermodynamics.
problem How organisms and machines adapt to their environment.
method Machine learning, information theory, and thermodynamics approaches to understanding and modeling adaptation.
result Unified view of adaptation in organisms and machines based on principles of machine learning, information theory, and thermodynamics.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
problem Understanding brain emotion circuit development during adolescence.
method Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) for multiple DAGs estimation.
result BiLiNGAM reveals unique developmental hub structures and group-specific patterns in emotion-related intra- and inter-modular connectivity.
The workshop explores AI and neuroscience, focusing on complex stimuli and neural network models.
problem Understanding and modeling brain function using AI and machine learning.
method Combining cognitive neuroscience with AI techniques like deep learning and attention mechanisms.
result The need for rich vector representations of stimuli for effective machine learning.
Mobile app for neonatal EEG interpretation helps non-experts diagnose brain health.
problem Limited EEG interpretation skills among neonatal healthcare professionals.
method Low-cost, low-power EEG acquisition system with AI-assisted sonification.
result Improves diagnostic capabilities of non-expert clinicians.
CNN-F uses generative feedback to improve neural networks' robustness to perturbations.
problem Neural networks' vulnerability to input perturbations like noise and attacks.
method Enforces self-consistency in neural networks by incorporating generative recurrent feedback.
result CNN-F shows significantly improved adversarial robustness compared to conventional CNNs.
Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spect…
End-to-end neural network extracts graph structure from EEG signals for improved emotional video classification.
problem Challenges in achieving accurate EEG classification for emotional video analysis.
method Proposes an end-to-end neural network model that learns an appropriate multi-layer graph structure from raw EEG signals.
result Improves performance in emotional video classification compared to manually defined connectivity structures.
PCI combines perception and control using Bayesian inference with object-based representations.
problem Separate perception and control in reinforcement learning.
method Joint Perception and Control as Inference (PCI) framework with Object-based Perception Control (OPC).
result OPC achieves good perceptual grouping quality and outperforms baselines in accumulated rewards.
Study the tradeoff between signal distortion and human perception over finite channels.
problem Characterize the distortion-perception tradeoff for finite channels with arbitrary metrics.
method Solve linear programming problems to compute the distortion-perception function and optimal reconstructions.
result DP function is piecewise linear in the perception index.
Inspired by brain's modality fusion, this paper detects active speakers from audio and video.
problem Detecting active speakers in noisy environments.
method Inspired by brain's superior colliculus, combines audio and visual data through specialized neural networks and a novel fusion layer.
result Achieved results greatly surpassing initial expectations, confirming the effectiveness of the proposed method.
Deep neural networks decode natural visual scenes from neural spikes.
problem Decoding visual scenes from neural spikes for brain-machine interfaces.
method Developed a novel spike-image decoder (SID) using deep neural networks.
result SID reconstructs natural visual scenes from neural spikes with high accuracy.
A framework isolates VQA reasoning from perception for better model evaluation.
problem Improper separation of visual perception and reasoning in VQA models.
method Introducing a framework and a top-down calibration technique to decouple reasoning from perception.
result Improved evaluation of VQA models by separating reasoning from perception.
End-to-end autonomous driving perception learns latent features for better performance.
problem Current autonomous driving systems are complex and require human engineering.
method Sequential latent representation learning for end-to-end perception.
result End-to-end perception model solves detection, tracking, localization, and mapping problems.
Geometric model explains music perception combining neuroscience and acoustics.
problem Rationalize and predict psycho-acoustic phenomena in music perception.
method Combining neuroscientific theories with acoustic observations, a geometric model of the space of all chords is created.
result The geometric model allows for rigorous studies of psychoacoustic quantities like roughness and harmonicity.
Convolutional attractor nets improve image completion and super-resolution.
problem Construct coherent neural states from noisy data.
method Revisit and extend attractor networks with convolutional bipartite architecture.
result Demonstrates potential for image completion and super-resolution.
Safe control for vehicles using learned perception from images.
problem Controlling autonomous vehicles with partial state information from images.
method Learned perception map and safe set design for a closed loop system.
result Generalization properties of the perception-control loop are favorable.
Self-training improves gradual domain adaptation with unlabeled data.
problem Improving machine learning models' adaptability to gradually shifting data distributions.
method Proved upper bounds on self-training error, highlighted the importance of regularization and label sharpening, and demonstrated algorithmic insights.
result Self-training works well for gradual shifts, especially with small Wasserstein-infinity distance.
Bayesian deep learning integrates perception and inference for higher-level intelligence.
problem Integrating perception and inference for tasks requiring higher-level intelligence.
method Unified probabilistic framework combining deep learning and Bayesian models.
result Integrating perception and inference leads to improved performance in tasks like recommender systems and topic models.
RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
problem Understanding the relationship between teachers' advising relationships and their perceptions of satisfaction and students.
method Proposed a novel joint model of network and item responses (JNIRM) with correlated latent variables.
result Teachers' advising relationships contribute more to satisfaction and students than to influence over educational policies.
Study how actions affect perception in embodied agents using group theory.
problem Understanding how actions influence perception in autonomous agents.
method Mathematical formalism of group theory applied to sensory commutativity of action sequences.
result Introduced Sensory Commutativity Probability (SCP) to measure action effects on perception.
Reconstruction-based learning produces uninformative features for perception tasks.
problem Misalignment between reconstruction-based learning and perception tasks.
method Investigated the impact of input space reconstruction on feature learning for perception tasks.
result Reconstruction-based learning allocates model capacity to a subspace with uninformative features for perception tasks.
The study estimates how changing words in sentences affects audience perception.
problem Estimating the causal effect of lexical choice on audience perception.
method Two classes of methods: quasi-experimental designs and classification problems.
result Algorithmic estimates align with randomized-control trials and can be transferred across domains.
Study uses deep learning to predict gender and analyze HPV vaccine perceptions on Twitter.
problem Analyzing gender differences in public perceptions on HPV vaccine using social media data.
method Convolutional neural network model trained on Twitter text for gender prediction, then applied to HPV vaccine related tweets.
result Identified gender differences in public perceptions on HPV vaccine, consistent with previous studies.
Mathematical models link perception and memory formation.
problem Linking perception and memory formation.
method Tensor decompositions and latent representations.
result Active semantic decoding process in perception.
Approach to develop visual perception in robots through sensorimotor interactions.
problem Developing autonomous perception in robots.
method Sensorimotor contingencies theory applied to robot exploration and learning.
result Captured sensorimotor regularities in a predictive model for visual field discovery.
Study examines perceptions and attitudes about breast cancer on Twitter.
problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.
PeL separates sensory interface optimization from decision learning.
problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.
PERCEPT detects changes in high-dimensional data streams using topological data analysis.
problem Detecting changes in high-dimensional data streams, especially when embedded in a low-dimensional space.
method Leverages topological data analysis to learn embedded topology as a point cloud via persistence diagrams, then applies non-parametric monitoring for detecting changes.
result Demonstrates efficient detection of online changes from high-dimensional data streams.
Logical scaffolds enhance AI software quality.
problem Improving AI component quality in software.
method Logical scaffolds as a method to improve AI components.
result Logical scaffolds can improve AI beyond perception systems.
Default-ERM shortcut learning persists even without additional information.
problem Default-ERM shortcut learning in perception tasks despite stable feature sufficiency.
method Studied linear perception task; developed margin control (MARG-CTRL) loss functions.
result Margin control mitigates shortcut learning on various tasks.
DGP learns speech recognition by modeling complex relationships between utterances.
problem Modeling complex relationships in speech recognition without relational data.
method Bayesian nonparametric deep learning method (DGP) that generates infinite probabilistic graphs.
result DGP successfully infers relationships among utterances without relational data during training.
Model shows neural nets can learn categorical perception.
problem Understanding how categorical perception arises in neural networks.
method Developed a neural network model to simulate learning-induced categorical perception.
result Neural nets can learn to perceive categories in a way similar to human perception.
Paper tackles robust spatial perception by handling outliers efficiently.
problem Robust spatial perception is challenged by incorrect data association (outliers).
method Proposes adaptive trimming algorithm to remove outliers efficiently.
result Adaptive trimming algorithm outperforms state-of-the-art methods across applications.
The study improves deep learning models for safer autonomous vehicles.
problem Robustness of deep neural network models in autonomous driving.
method Analyzes and proposes solutions for deep learning model robustness.
result Enhanced deep learning models for safer autonomous vehicles.
New method allows a robot to perceive space dimensions without prior knowledge.
problem Limitation of previous methods in perceiving space dimensions with small movements.
method Non-linear dimension estimation method.
result Robots can now perceive space dimensions with larger movements.
This research explores a modified VAE model to learn disentangled representations for object recognition.
problem Learning invariant representations for object recognition from diverse appearances.
method Develops a modified Variational Autoencoder (β-VAE) to enforce disentangled representations using variational inference. result Demonstrates that the incompatibility between β-VAE's conditional independence and latent variable independence leads to non-monotonic inference performance. A technique finds adversarial examples for deep neural networks using human perception.
problem Finding adversarial examples for deep neural networks without access to internal structure.
method Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with perception-in-the-loop.
result CMA-ES can find adversarial examples with human feedback, showing favorable performance.
This study examines LiDAR spoofing attacks on autonomous vehicle perception.
problem Security vulnerabilities in LiDAR-based perception systems in autonomous driving.
method Formulated as an optimization problem, designed input perturbation and objective functions, combined optimization and global sampling.
result Attack success rates improved to around 75% through strategic input perturbation.