neural representations
The way in which data is encoded in the hidden layers of a neural network. These representations capture complex features and patterns in the data that are essential for tasks like classification and generation.
- $i$MIND: Insightful Multi-subject Invariant Neural Decoding
- Contribution of task-irrelevant stimuli to drift of neural representations
- Decomposing stimulus-specific sensory neural information via diffusion models
- Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshing
- LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
- One SPACE to Rule Them All: Jointly Mitigating Factuality and Faithfulness Hallucinations in LLMs
- RNNs perform task computations by dynamically warping neural representations
- Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain
- Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain
- ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding