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Found 18 papers, 7 papers with code
Learning to Reason via Mixture-of-Thought for Logical Reasoning
To fill in this gap, we propose Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three complementary modalities: natural language, code, and a newly introduced symbolic modality, truth-table, which systematically enumerates logical cases and partially mitigates key failure modes in natural language reasoning.
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs).
Towards Optimal Multi-draft Speculative Decoding
For a fixed draft sampling method, the optimal acceptance rate is a solution to an optimal transport problem, but the complexity of this problem makes it difficult to solve for the optimal acceptance rate and measure the gap between existing verification algorithms and the theoretical upper bound.
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation
The emergence of Large Language Models (LLMs) has advanced the multilingual machine translation (MMT), yet the Curse of Multilinguality (CoM) remains a major challenge.
Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization
Large language models (LLMs) exhibit exceptional performance across various downstream tasks.
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
First, we introduce a predictor-corrector learning framework to minimize truncation errors, which consists of a high-order predictor and a multistep corrector.
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution
Authorship attribution aims to identify the origin or author of a document.
Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network
This study addresses the critical challenge of predicting the Q-distribution in long-term stable nuclear fusion task, a key component for advancing clean energy solutions.
TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data.
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete.
PartialFormer: Modeling Part Instead of Whole for Machine Translation
In this work, we emphasize the importance of hidden dimensions in designing lightweight FFNs, a factor often overlooked in previous architectures.
Ranked #23 on
Machine Translation
on WMT2014 English-German
EIT: Enhanced Interactive Transformer
Two principles: the complementary principle and the consensus principle are widely acknowledged in the literature of multi-view learning.
Learning Multiscale Transformer Models for Sequence Generation
In this work, we define those scales in different linguistic units, including sub-words, words and phrases.
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset
Unsupervised SR methods are required that do not need paired LR and HR images.
Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes.
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes.
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.
