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Found 15 papers, 2 papers with code
HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020.
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation.
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge.
RAEE: A Robust Retrieval-Augmented Early Exiting Framework for Efficient Inference
Finally, based on the pre-built retrieval database, RAEE leverages the retrieved similar data's exiting information to guide the backbone model to exit at the layer, which is predicted by the approximated distribution.
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks.
Natural Language Understanding
Neural Architecture Search
+5
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Further, the block sparse recovery property is analyzed for wide bandwidth signals.
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper.
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
In this paper, we present LightSeq2, a system to accelerate training for a general family of Transformer models on GPUs.
Atomic configuration controlled photocurrent in van der Waals homostructures
Conventional photocurrents at a p-n junction depend on macroscopic built-in fields and are typically insensitive to the microscopic details of a crystal's atomic configuration.
Mesoscale and Nanoscale Physics
LightSeq: A High Performance Inference Library for Transformers
Transformer, BERT and their variants have achieved great success in natural language processing.
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
A Deep Learning-Based System for PharmaCoNER
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs.
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range.
From Shading to Local Shape
We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch.
