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Found 10 papers, 7 papers with code
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP.
Zero-Shot Stitching in Reinforcement Learning using Relative Representations
We build upon the recent relative representations framework and adapt it for Visual RL.
Latent Space Translation via Semantic Alignment
While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases.
Accelerating Transformer Inference for Translation via Parallel Decoding
We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference.
Attention-likelihood relationship in transformers
We analyze how large language models (LLMs) represent out-of-context words, investigating their reliance on the given context to capture their semantics.
Bootstrapping Parallel Anchors for Relative Representations
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications.
Relative representations enable zero-shot latent space communication
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations.
Sparse Vicious Attacks on Graph Neural Networks
In this work, we focus on a specific, white-box attack to GNN-based link prediction models, where a malicious node aims to appear in the list of recommended nodes for a given target victim.
Metric Based Few-Shot Graph Classification
In this work, we tackle the problem of few-shot graph classification, showing that equipping a simple distance metric learning baseline with a state-of-the-art graph embedder allows to obtain competitive results on the task. While the simplicity of the architecture is enough to outperform more complex ones, it also allows straightforward additions.
