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Found 25 papers, 11 papers with code
Generalizing Math Word Problem Solvers via Solution Diversification
In this paper, we design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator.
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning
It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes.
Temporal knowledge graph representation learning with local and global evolutions
Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKG to low-dimensional vector space while preserving the evolutionary nature of TKG.
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
SMDT: Cross-View Geo-Localization with Image Alignment and Transformer
Next, we simultaneously train dual conditional generative adversarial nets by taking the semantic segmentation images and converted images as input to synthesize the aerial image with ground view style.
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.
Efficient divide-and-conquer registration of UAV and ground LiDAR point clouds through canopy shape context
Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration.
Mining Contextual Information Beyond Image for Semantic Segmentation
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Math word problem (MWP) solving faces a dilemma in number representation learning.
Ranked #3 on
Math Word Problem Solving
on MathQA
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
It enables learning high-quality Laplacian representations that faithfully approximate the ground truth.
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-Training for Vision-Language Representation
There are two key designs in it, one is the weight-sharing transformer on top of the visual and textual encoders to align text and image semantically, the other is three kinds of contrastive learning designed for sharing knowledge between different modalities.
Contrast and Order Representations for Video Self-Supervised Learning
To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.
Is normalization indispensable for training deep neural network?
In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation.
Improving Generalization in Reinforcement Learning with Mixture Regularization
Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments.
E-FCNN for tiny facial expression recognition
To effectively leverage the texture information of faces, we design a novel three-stream super-resolution network, which is embedded with an edge-enhancement block as one branch.
Temporal Context Aggregation for Video Retrieval with Contrastive Learning
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc.
Graph-to-Tree Learning for Solving Math Word Problems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #6 on
Math Word Problem Solving
on MAWPS
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #17 on
Click-Through Rate Prediction
on Criteo
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
BIG-bench Machine Learning
One-shot visual object segmentation
+2
Qiniu Submission to ActivityNet Challenge 2018
We also propose new non-local-based models for further improvement on the recognition accuracy.
Learning in High-Dimensional Multimedia Data: The State of the Art
Given the high dimensionality and the high complexity of multimedia data, it is important to investigate new machine learning algorithms to facilitate multimedia data analysis.
Zero-Shot Hashing via Transferring Supervised Knowledge
Supervised knowledge e. g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions.
Learning to Point and Count
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.
Multiple Granularity Descriptors for Fine-Grained Categorization
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs.
