Ivan Kobyzev | DeepAI

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∙ 07/11/2023

Attribute Controlled Dialogue Prompting

Prompt-tuning has become an increasingly popular parameter-efficient met...

0 Runcheng Liu, et al. ∙

∙ 05/08/2023

LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization

Regularization techniques are crucial to improving the generalization pe...

0 Peng Lu, et al. ∙

∙ 03/24/2023

Mathematical Challenges in Deep Learning

Deep models are dominating the artificial intelligence (AI) industry sin...

0 Vahid Partovi Nia, et al. ∙

∙ 12/20/2022

KronA: Parameter Efficient Tuning with Kronecker Adapter

Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream ...

0 Ali Edalati, et al. ∙

∙ 12/12/2022

Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization

Knowledge Distillation (KD) has been extensively used for natural langua...

0 Aref Jafari, et al. ∙

∙ 12/12/2022

Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging

Knowledge Distillation (KD) is a commonly used technique for improving t...

0 Peng Lu, et al. ∙

∙ 10/14/2022

DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

With the ever-growing size of pre-trained models (PMs), fine-tuning them...

0 Mojtaba Valipour, et al. ∙

∙ 06/30/2022

Learning Functions on Multiple Sets using Multi-Set Transformers

We propose a general deep architecture for learning functions on multipl...

0 Kira Selby, et al. ∙

∙ 05/25/2022

Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models

Knowledge Distillation (KD) is a prominent neural model compression tech...

0 Ivan Kobyzev, et al. ∙

∙ 10/16/2021

Equivariant Discrete Normalizing Flows

At its core, generative modeling seeks to uncover the underlying factors...

0 Avishek Joey Bose, et al. ∙

∙ 04/22/2020

Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

Learning disentangled representations of real world data is a challengin...

0 Vikash Balasubramanian, et al. ∙

∙ 03/07/2020

Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory

State-of-the-art neural dialogue systems excel at syntactic and semantic...

0 Nabiha Asghar, et al. ∙

∙ 08/25/2019

Normalizing Flows: Introduction and Ideas

Normalizing Flows are generative models which produce tractable distribu...

0 Ivan Kobyzev, et al. ∙

∙ 07/10/2019

Tails of Triangular Flows

Triangular maps are a construct in probability theory that allows the tr...

2 Priyank Jaini, et al. ∙

∙ 05/27/2019

Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey

Graphs arise naturally in many real-world applications including social ...

0 Seyed Mehran Kazemi, et al. ∙

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