In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Example Input: Synthesizing user-intended programs from a small number of input-output exam-
 ples is a challenging problem with several important applications like spreadsheet
 manipulation, data wrangling and code refactoring. Existing synthesis systems
 either completely rely on deductive logic techniques that are extensively hand-
 engineered or on purely statistical models that need massive amounts of data, and in
 general fail to provide real-time synthesis on challenging benchmarks. In this work,
 we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique
 that combines the best of both symbolic logic techniques and statistical models.
 Thus, it produces programs that satisfy the provided specifications by construction
 and generalize well on unseen examples, similar to data-driven systems. Our
 technique effectively utilizes the deductive search framework to reduce the learning
 problem of the neural component to a simple supervised learning setup. Further,
 this allows us to both train on sparingly available real-world data and still leverage
 powerful recurrent neural network encoders. We demonstrate the effectiveness
 of our method by evaluating on real-world customer scenarios by synthesizing
 accurate programs with up to 12× speed-up compared to state-of-the-art systems.
Example Output: We integrate symbolic (deductive) and statistical (neural-based) methods to enable real-time program synthesis with almost perfect generalization from 1 input-output example.

Example Input: In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e.g., images obtained from self-supervised robot interaction. VP algorithms essentially combine data-driven perception and planning, and are important for robotic manipulation and navigation domains, among others. A recent and promising approach to VP is the semi-parametric topological memory (SPTM) method, where image samples are treated as nodes in a graph, and the connectivity in the graph is learned using deep image classification. Thus, the learned graph represents the topological connectivity of the data, and planning can be performed using conventional graph search methods. However, training SPTM necessitates a suitable loss function for the connectivity classifier, which requires non-trivial manual tuning. More importantly, SPTM is constricted in its ability to generalize to changes in the domain, as its graph is constructed from direct observations and thus requires collecting new samples for planning. In this paper, we propose Hallucinative Topological Memory (HTM), which overcomes these shortcomings. In HTM, instead of training a discriminative classifier we train an energy function using contrastive predictive coding. In addition, we learn a conditional VAE model that generates samples given a context image of the domain, and use these hallucinated samples for building the connectivity graph, allowing for zero-shot generalization to domain changes. In simulated domains, HTM outperforms conventional SPTM and visual foresight methods in terms of both plan quality and success in long-horizon planning.
Example Output: We propose Hallucinative Topological Memory (HTM), a visual planning algorithm that can perform zero-shot long horizon planning in new environments. 

Example Input: One of the main challenges of deep learning methods is the choice of an appropriate training strategy. In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures. In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network. We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding. This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task. This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance.
Example Output:
We propose an additional training step, called post-training, which computes optimal weights for the last layer of the network.