NeurIPS 2024 Past Other
UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models
UniReps
- Submission deadline
- Sep 24, 2024, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (68)
Fetched from OpenReview (v2) on 2026-06-10.
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A Cognitive Framework for Learning Debiased and Interpretable Representations via Debiasing Global Workspace
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A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field
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Adapter to facilitate Foundation Model Communication for DLO Instance Segmentation
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An Information Criterion for Controlled Disentanglement of Multimodal Data
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Artificial Neural Networks Generate Human-like Continuous Speech Perception
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Auxiliary objective improves generalization performance but reduces model specification for low-data neuroimaging-based brain age prediction
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Challenges in Explaining Representational Similarity through Identifiability
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Circular Learning Provides Biological Plausibility
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Comparing Representations in Static and Dynamic Vision Models to the Human Brain
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Comparing the local information geometry of image representations
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Conic Activation Functions
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Connecting Neural Models Latent Geometries with Relative Geodesic Representations
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CopRA: A Progressive LoRA Training Strategy
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Correlating Variational Autoencoders Natively For Multi-View Imputation
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Decision-margin consistency: a principled metric for human and machine performance alignment
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Delays in generalization match delayed changes in representational geometry
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DFM: Interpolant-free Dual Flow Matching
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DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features
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Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations
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Emergence of Text Semantics in CLIP Image Encoders
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Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis
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Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models
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Fast Imagic: Solving Overfitting in Text-guided Image Editing via Disentangled UNet with Forgetting Mechanism and Unified Vision-Language Optimization
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Federated GNNs for EEG-Based Stroke Assessment
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Finding Symmetry in Neural Network Parameter Spaces
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From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
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Hybrid Dynamic High-Order Functional Correlations and Divisive Normalization for Improved Classification of Schizophrenia and Bipolar Disorder
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Hypernetworks for image recontextualization
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Improving Model Merging with Natural Niches
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Improving OOD Generalization of Pre-trained Encoders via Aligned Embedding-Space Ensembles
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Inducing Human-like Biases in Moral Reasoning Language Models
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Invariant Learning with Annotation-free Environments
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Investigating the role of modality and training objective on representational alignment between transformers and the brain
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It's All Relative: Relative Uncertainty in Latent Spaces using Relative Representations
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Joint Learning for Visual Reconstruction from the Brain Activity: Hierarchical Representation of Image Perception with EEG-Vision Transformer
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Language decoding from human brain activity via contrastive learning
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Locality-aware Concept Bottleneck Model
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Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks
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M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
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Modern Hopfield Networks meet Encoded Neural Representations - Addressing Practical Considerations
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Monkey See, Model Knew: Large Language Models accurately Predict Human AND Macaque Visual Brain Activity
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Multi-task Learning yields Disentangled World Models: Impact and Implications
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Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities
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On the cognitive alignment between humans and machines
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Position: Maximizing Neural Regression Scores May Not Identify Good Models of the Brain
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Random Propagations in GNNs
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Relative Representations: Topological and Geometric Perspectives
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Representation Learning of Structured Data for Medical Foundation Models
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Representation with a capital 'R': measuring functional alignment with causal perturbation
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Rethinking Fine-tuning Through Geometric Perspective
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Revealing spatial-frequency channels in an ensemble encoding model of human fMRI
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Self-Supervised Pre-training of Spiking Neural Networks by Contrasting Events and Frames
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Shared Recurrent Memory Improves Multi-agent Pathfinding
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Small-scale adversarial perturbations expose differences between predictive encoding models of human fMRI responses
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Task-Relevant Covariance from Manifold Capacity Theory Improves Robustness in Deep Networks
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Topology Preserving Regularization for Independent Training of Inter-operable Models
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Understanding Memorization using Representation Similarity Analysis and Model Stitching
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Understanding Permutation Based Model Merging with Feature Visualizations
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Understanding Variational Autoencoders with Intrinsic Dimension and Information Imbalance
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Unifying Causal Representation Learning with the Invariance Principle
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Unsupervised Learning of Categorical Structure
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Unsupervised Modality Adaptation in Human Action Recognition via Cross-modal Representation Learning
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Video decoding from human fMRI data with a multi-stream sensory approach
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Vision and language representations in multimodal AI models and human social brain regions during natural movie viewing
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VISTA: A Panoramic View of Neural Representations
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What Representational Similarity Measures Imply about Decodable Information
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Winning Tickets from Random Initialization: Aligning Masks for Sparse Training
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Workshop Submission: Towards Making Untrainable Networks Trainable