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Found 36 papers, 12 papers with code
Seedream 3.0 Technical Report
At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework.
Attention Reveals More Than Tokens: Training-Free Long-Context Reasoning with Attention-guided Retrieval
Interestingly, we observe that the internal attention weights from the generated CoT tokens can effectively ground implicit facts, even when these facts are not explicitly recalled.
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
To address these limitations, we present Seedream 2. 0, a native Chinese-English bilingual image generation foundation model that excels across diverse dimensions, which adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering.
Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning
There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions.
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada.
Object Detection for Medical Image Analysis: Insights from the RT-DETR Model
Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges.
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses.
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening.
Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity
Moreover, the prevalent use of the Black-Scholes formula in hybrid models fails to accurately capture the discontinuity in the price process, limiting model performance, especially under scarce data conditions.
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction.
Attention Mechanism and Context Modeling System for Text Mining Machine Translation
This paper advances a novel architectural schema anchored upon the Transformer paradigm and innovatively amalgamates the K-means categorization algorithm to augment the contextual apprehension capabilities of the schema.
Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
Our pretrained models demonstrate superior performance (against existing acoustic models pretrained with general audio on 16 out of 19 tasks) and generalizability (to unseen datasets and new respiratory audio modalities).
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
Large language models have repeatedly shown outstanding performance across diverse applications.
Monocular Localization with Semantics Map for Autonomous Vehicles
To balance efficiency and accuracy, we propose a novel lightweight visual semantic localization algorithm that employs stable semantic features instead of low-level texture features.
Tool-Planner: Task Planning with Clusters across Multiple Tools
Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method.
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations
Object detection methods under known single degradations have been extensively investigated.
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Furthermore, miscellaneous resilient coordination problems are discussed in this survey.
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
A Closed-loop Brain-Machine Interface SoC Featuring a 0.2$μ$J/class Multiplexer Based Neural Network
This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation.
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks.
ClusterLLM: Large Language Models as a Guide for Text Clustering
First, we prompt ChatGPT for insights on clustering perspective by constructing hard triplet questions <does A better correspond to B than C>, where A, B and C are similar data points that belong to different clusters according to small embedder.
Toward Unsupervised Realistic Visual Question Answering
To resolve the first drawback, we propose a new testing dataset, RGQA, which combines AQs from an existing VQA dataset with around 29K human-annotated UQs.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
Measurement of carbon finance level and exploration of its influencing factors
Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming.
New Intent Discovery with Pre-training and Contrastive Learning
Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.
Effectiveness of Pre-training for Few-shot Intent Classification
This paper investigates the effectiveness of pre-training for few-shot intent classification.
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.
Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition
We also find that texture features have an impelling effect on these tasks and that the pre-trained ImageNet model has good performance in extracting texture features.
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.
Predict Future Sales using Ensembled Random Forests
This is a method report for the Kaggle data competition 'Predict future sales'.
