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Found 11 papers, 3 papers with code
Course Correcting Koopman Representations
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space.
On Conditional and Compositional Language Model Differentiable Prompting
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Crosslingual conditional generation (e. g., machine translation) has long enjoyed the benefits of scaling.
Using Graph Algorithms to Pretrain Graph Completion Transformers
While using our new path-finding algorithm as a pretraining signal provides 2-3% MRR improvements, we show that pretraining on all signals together gives the best knowledge graph completion results.
Towards Neural Functional Program Evaluation
This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional programming languages.
Mem2Mem: Learning to Summarize Long Texts with Memory Compression and Transfer
We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization.
Learning to Summarize Long Texts with Memory Compression and Transfer
We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization.
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Through this construction (a hypernetwork adapter), we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed.
Ranked #1 on
Natural Language Inference
on SciTail
On the impressive performance of randomly weighted encoders in summarization tasks
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models and compare their performance with that of fully-trained encoders on the task of abstractive summarization.
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization.
Ranked #18 on
Text Summarization
on Pubmed
