42 research outputs found
Correlated Cascades: Compete or Cooperate
In real world social networks, there are multiple cascades which are rarely
independent. They usually compete or cooperate with each other. Motivated by
the reinforcement theory in sociology we leverage the fact that adoption of a
user to any behavior is modeled by the aggregation of behaviors of its
neighbors. We use a multidimensional marked Hawkes process to model users
product adoption and consequently spread of cascades in social networks. The
resulting inference problem is proved to be convex and is solved in parallel by
using the barrier method. The advantage of the proposed model is twofold; it
models correlated cascades and also learns the latent diffusion network.
Experimental results on synthetic and two real datasets gathered from Twitter,
URL shortening and music streaming services, illustrate the superior
performance of the proposed model over the alternatives
Orthogonal Gradient Descent for Continual Learning
Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a continual or sequential manner, however, neural networks suffer from the problem of catastrophic forgetting; they forget how to solve previous tasks after being trained on a new task, despite having the essential capacity to solve both tasks if they were trained on both simultaneously. In this paper, we propose to address this issue from a parameter space perspective and study an approach to restrict the direction of the gradient updates to avoid forgetting previously-learned data. We present the Orthogonal Gradient Descent (OGD) method, which accomplishes this goal by projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task. Our approach utilizes the high capacity of a neural network more efficiently and does not require storing the previously learned data that might raise privacy concerns. Experiments on common benchmarks reveal the effectiveness of the proposed OGD method
Orthogonal Gradient Descent for Continual Learning
Neural networks are achieving state of the art and sometimes super-human
performance on learning tasks across a variety of domains. Whenever these
problems require learning in a continual or sequential manner, however, neural
networks suffer from the problem of catastrophic forgetting; they forget how to
solve previous tasks after being trained on a new task, despite having the
essential capacity to solve both tasks if they were trained on both
simultaneously. In this paper, we propose to address this issue from a
parameter space perspective and study an approach to restrict the direction of
the gradient updates to avoid forgetting previously-learned data. We present
the Orthogonal Gradient Descent (OGD) method, which accomplishes this goal by
projecting the gradients from new tasks onto a subspace in which the neural
network output on previous task does not change and the projected gradient is
still in a useful direction for learning the new task. Our approach utilizes
the high capacity of a neural network more efficiently and does not require
storing the previously learned data that might raise privacy concerns.
Experiments on common benchmarks reveal the effectiveness of the proposed OGD
method