12,207 research outputs found
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW),
201
Deep Neuroevolution of Recurrent and Discrete World Models
Neural architectures inspired by our own human cognitive system, such as the
recently introduced world models, have been shown to outperform traditional
deep reinforcement learning (RL) methods in a variety of different domains.
Instead of the relatively simple architectures employed in most RL experiments,
world models rely on multiple different neural components that are responsible
for visual information processing, memory, and decision-making. However, so far
the components of these models have to be trained separately and through a
variety of specialized training methods. This paper demonstrates the surprising
finding that models with the same precise parts can be instead efficiently
trained end-to-end through a genetic algorithm (GA), reaching a comparable
performance to the original world model by solving a challenging car racing
task. An analysis of the evolved visual and memory system indicates that they
include a similar effective representation to the system trained through
gradient descent. Additionally, in contrast to gradient descent methods that
struggle with discrete variables, GAs also work directly with such
representations, opening up opportunities for classical planning in latent
space. This paper adds additional evidence on the effectiveness of deep
neuroevolution for tasks that require the intricate orchestration of multiple
components in complex heterogeneous architectures
Labeled Memory Networks for Online Model Adaptation
Augmenting a neural network with memory that can grow without growing the
number of trained parameters is a recent powerful concept with many exciting
applications. We propose a design of memory augmented neural networks (MANNs)
called Labeled Memory Networks (LMNs) suited for tasks requiring online
adaptation in classification models. LMNs organize the memory with classes as
the primary key.The memory acts as a second boosted stage following a regular
neural network thereby allowing the memory and the primary network to play
complementary roles. Unlike existing MANNs that write to memory for every
instance and use LRU based memory replacement, LMNs write only for instances
with non-zero loss and use label-based memory replacement. We demonstrate
significant accuracy gains on various tasks including word-modelling and
few-shot learning. In this paper, we establish their potential in online
adapting a batch trained neural network to domain-relevant labeled data at
deployment time. We show that LMNs are better than other MANNs designed for
meta-learning. We also found them to be more accurate and faster than
state-of-the-art methods of retuning model parameters for adapting to
domain-specific labeled data.Comment: Accepted at AAAI 2018, 8 page
- …