REVIEW 5 cited by
Relative representations enable zero-shot latent space communication
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training hyperparameters, or other sources of randomness in the training phase may induce incoherent latent spaces that hinder any form of reuse. Nevertheless, we empirically observe that, under the same data and modeling choices, the angles between the encodings within distinct latent spaces do not change. In this work, we propose the latent similarity between each sample and a fixed set of anchors as an alternative data representation, demonstrating that it can enforce the desired invariances without any additional training. We show how neural architectures can leverage these relative representations to guarantee, in practice, invariance to latent isometries and rescalings, effectively enabling latent space communication: from zero-shot model stitching to latent space comparison between diverse settings. We extensively validate the generalization capability of our approach on different datasets, spanning various modalities (images, text, graphs), tasks (e.g., classification, reconstruction) and architectures (e.g., CNNs, GCNs, transformers).
Forward citations
Cited by 5 Pith papers
-
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
TAQ estimates per-layer importance from hidden representations and output sensitivity on task calibration data to allocate mixed precision in a training-free PTQ setting, outperforming task-agnostic baselines on accur...
-
Latent Space Alignment for AI-Native MIMO Semantic Communications
Joint MIMO precoder/decoder optimization for latent space alignment outperforms disjoint semantic alignment and channel equalization in simulations.
-
What are you sinking? A geometric approach on attention sink
Attention sinks in transformers are reinterpreted as geometric reference frames, with three architecture-dependent types: centralized, distributed, and bidirectional.
-
RIS-aided Latent Space Alignment for Semantic Channel Equalization
RIS-aided joint physical and semantic channel equalization, solved by alternating optimization or neural networks, outperforms separate alignment-and-transmission baselines in MIMO semantic communication simulations.
-
Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Using Parseval-frame projections onto shared anchor features, a receiver can approximately reconstruct the latent vectors of an unseen, independently trained encoder; a Lyapunov scheduler then allocates bandwidth, CPU...
Discussion (0). Sign in to comment.