64,517 research outputs found
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Sequential Recommendation (SR) has received increasing attention due to its
ability to capture user dynamic preferences. Recently, Contrastive Learning
(CL) provides an effective approach for sequential recommendation by learning
invariance from different views of an input. However, most existing data or
model augmentation methods may destroy semantic sequential interaction
characteristics and often rely on the hand-crafted property of their
contrastive view-generation strategies. In this paper, we propose a
Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for
sequential recommendation, which applies the meta-optimized two-step training
strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first
introduces a simple yet effective augmentation method called
Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational
AutoEncoders (VAE) as the view generator and can constitute contrastive views
while preserving the original sequence's semantics. Next, the model employs a
meta-optimized two-step training strategy, which aims to adaptively generate
contrastive views without relying on manually designed view-generation
techniques. Finally, we evaluate our proposed method Meta-SGCL using three
public real-world datasets. Compared with the state-of-the-art methods, our
experimental results demonstrate the effectiveness of our model and the code is
available
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters
Sequential reasoning is a complex human ability, with extensive previous
research focusing on gaming AI in a single continuous game, round-based
decision makings extending to a sequence of games remain less explored.
Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant
expert demonstrations, provides an excellent environment for multi-player
round-based sequential reasoning. In this work, we propose a Sequence Reasoner
with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies
behind the round-based purchasing decisions. We adopt few-shot learning to
sample multiple rounds in a match, and modified model agnostic meta-learning
algorithm Reptile for the meta-learning loop. We formulate each round as a
multi-task sequence generation problem. Our state representations combine
action encoder, team encoder, player features, round attribute encoder, and
economy encoders to help our agent learn to reason under this specific
multi-player round-based scenario. A complete ablation study and comparison
with the greedy approach certify the effectiveness of our model. Our research
will open doors for interpretable AI for understanding episodic and long-term
purchasing strategies beyond the gaming community.Comment: 16th AAAI Conference on Artificial Intelligence and Interactive
Digital Entertainment (AIIDE-20
Generalized Stacked Sequential Learning
In many supervised learning problems, it is assumed that data is independent and identically distributed. This assumption does not hold true in many real cases, where a neighboring pair of examples and their labels exhibit some kind of relationship. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In the literature, there are different approaches that try to capture and exploit this correlation by means of different methodologies. In this thesis we focus on meta-learning strategies and, in particular, the stacked sequential learning (SSL) framework.The main contribution of this thesis is to generalize the SSL highlighting the key role of how to model theneighborhood interactions. We propose an effective and efficient way of capturing and exploiting sequentialcorrelations that take into account long-range interactions. We tested our method on several tasks: text lineclassification, image pixel classification, multi-class classification problems and human pose segmentation.Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning as well as off-the-shelf graphical models such conditional random fields
Few-Shot Classification with Contrastive Learning
A two-stage training paradigm consisting of sequential pre-training and
meta-training stages has been widely used in current few-shot learning (FSL)
research. Many of these methods use self-supervised learning and contrastive
learning to achieve new state-of-the-art results. However, the potential of
contrastive learning in both stages of FSL training paradigm is still not fully
exploited. In this paper, we propose a novel contrastive learning-based
framework that seamlessly integrates contrastive learning into both stages to
improve the performance of few-shot classification. In the pre-training stage,
we propose a self-supervised contrastive loss in the forms of feature vector
vs. feature map and feature map vs. feature map, which uses global and local
information to learn good initial representations. In the meta-training stage,
we propose a cross-view episodic training mechanism to perform the nearest
centroid classification on two different views of the same episode and adopt a
distance-scaled contrastive loss based on them. These two strategies force the
model to overcome the bias between views and promote the transferability of
representations. Extensive experiments on three benchmark datasets demonstrate
that our method achieves competitive results.Comment: To appear in ECCV 202
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