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Found 14 papers, 5 papers with code
REPS: Reconstruction-based Point Cloud Sampling
Our method outperforms previous approaches in preserving the structural features of the sampled point clouds.
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance.
On Compositionality and Improved Training of NADO
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
Unsupervised Melody-to-Lyric Generation
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition
Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs).
Unsupervised Melody-Guided Lyrics Generation
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
However, it is challenging for crossbar architecture to exploit the sparsity in the DNN.
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point
Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN).
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.
Hierarchical Routing Mixture of Experts
Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts.
Neural Regression Trees
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Neural Regression Tree
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Speaker identification from the sound of the human breath
The goal of this paper is to demonstrate that breath sounds are indeed bio-signatures that can be used to identify speakers.
