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Found 38 papers, 20 papers with code
Business as \textit{Rule}sual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
We evaluate ExIde using 12 state-of-the-art (SOTA) LLMs on the BPRF dataset, benchmarking performance on both rule extraction and dependency classification tasks of current LLMs.
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) leverages large language models (LLMs) combined with external contexts to enhance the accuracy and reliability of generated responses.
Towards deployment-centric multimodal AI beyond vision and language
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering.
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story Videos
However, existing DVU datasets rarely organize questions according to these story topics, making them difficult to comprehensively assess VideoQA models' DVU capability of complex storylines.
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication.
DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models
This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation.
Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning
Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies.
Adaptive Retrieval-Augmented Generation for Conversational Systems
Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses.
Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated
Instead, we introduce a novel ternary text classification scheme, adding an "undecided" category for texts that could be attributed to either source, and we show that this new category is crucial to understand how to make the detection result more explainable to lay users.
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data
The aim is to provide comprehensive and rich information for researchers interested in automatic clinical report generation and medical image analysis, especially when using multimodal inputs, and assist them in developing new algorithms to advance the field.
MrRegNet: Multi-resolution Mask Guided Convolutional Neural Network for Medical Image Registration with Large Deformations
To address this challenge, this paper proposes a mask-guided encoder-decoder DCNN-based image registration method, named as MrRegNet.
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models
Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which aims to predict the ground-truth transcription from the decoded N-best hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System
Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics.
Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions
By updating these vectors within MLP and recalibrating attention patterns to neutralise the preference for the first choice 'A', we effectively mitigate the anchored bias.
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
This paper introduces Zoo-MSFDA, a more general setting that allows each source domain to offer a zoo of multiple source models with different architectures.
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the rich information in N-best candidates to generate a higher-quality translation result.
Ranked #1 on
Machine Translation
on FLoRes-200
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output.
Ranked #4 on
Speech Recognition
on WSJ eval92
(using extra training data)
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition
In this work, we investigate the noise-invariant visual modality to strengthen robustness of AVSR, which can adapt to any testing noises while without dependence on noisy training data, a. k. a., unsupervised noise adaptation.
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition
In this paper, we aim to learn the shared representations across modalities to bridge their gap.
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
However, most existing AVSR approaches simply fuse the audio and visual features by concatenation, without explicit interactions to capture the deep correlations between them, which results in sub-optimal multimodal representations for downstream speech recognition task.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition
In this paper, we propose a simple yet effective approach called gradient remedy (GR) to solve interference between task gradients in noise-robust speech recognition, from perspectives of both angle and magnitude.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
The final trained model was also evaluated on an independent test set by the CMRxMotion organisers, which achieved the classification accuracy of 72. 5% and Cohen's Kappa of 0. 6309 (ranked top 1 in this grand challenge).
On the Latent Holes of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
On the Latent Holes 🧀 of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
Affective Decoding for Empathetic Response Generation
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems.
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
The proposed method was evaluated on a public brain MRI data set for age estimation.
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences.
DGST: a Dual-Generator Network for Text Style Transfer
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation.
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).
A Stable Variational Autoencoder for Text Modelling
Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
Ranked #7 on
Image Generation
on CelebA 256x256
(FID metric)
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition.
Ranked #4 on
Dialogue Act Classification
on Switchboard corpus
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes.
