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Found 8 papers, 2 papers with code
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification
Our work stands out by offering a one-stop empirical benchmark of multiplicity across various dimensions of model design and its impact on a diverse set of trustworthy metrics.
On The Impact of Machine Learning Randomness on Group Fairness
We investigate the impact on group fairness of different sources of randomness in training neural networks.
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs
Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency.
Free Lunch for Co-Saliency Detection: Context Adjustment
We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.
Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization
Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data.
Abstractive Dialogue Summarization
Abstractive Text Summarization
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VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data.
