85 citations · 145 across the 8 of their papers we have counts for
22 papers
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
Jannik Wolff, Tassilo Klein, Moin Nabi +2
Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthc…
SCD: Self-Contrastive Decorrelation for Sentence Embeddings
Tassilo Klein, Moin Nabi
In this paper, we propose Self-Contrastive Decorrelation (SCD), a self-supervised approach. Given an input sentence, it optimizes a joint self-contrastive and decorrelation objecti…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
A Unified Objective for Novel Class Discovery
Enrico Fini, Enver Sangineto, Stéphane Lathuilière +3
In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labele…
Attention-based Contrastive Learning for Winograd Schemas
Tassilo Klein, Moin Nabi
Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This pap…
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models
Tassilo Klein, Moin Nabi
Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense rea…