Dinner

Dinner is an interactive exhibition which presents appropriated works of art collected and hung in a clustered salon style, as well as a fully realized recreation based on a 16th century Dutch banquet still-life, which presents guests with meats, cheeses, fruits, vegetables, breads, and wine to share and imbibe. Dining ware is provided for guests at the entrance to the exhibit, as are suggested topics of conversation, which are presented on slips of paper for guests to carry with them throughout their time in the space. Within the collection of wall-mounted works are references to ancient Greek and Roman marble statues, portraits of European elites spanning the 17th and 18th century, and more modern children’s cartoons from the 1980s and 90s. The disparate references align into a singular motif by using repetition of color, pose, framing and material within the artworks. This installation explores themes of beauty, class, privilege, history, excess, and humor while providing a space for cultivating deeper conversations on said subjects through the act of sharing, looking, and eating

Approaches for Incorporating a Variety of Metadata in Transformer Operation

A plain transformer model typically leverages only one piece of metadata - position encoding - directly in the transformer model. The use of transformers typically involves expensive and complex external scaffolding before or after output generation to avoid issues such as hallucination, irrelevance, etc. This disclosure describes techniques to incorporate a variety of metadata types into the native architecture of transformer models. The additional signals can help avoid hallucinations, improve relevance, and minimize the need of expensive external scaffolding. Generalizing transformer operation to incorporate a diversity of metadata can be achieved in various ways such as adding a metadata embedding layer, conditioning self-attention on the metadata, conditioning with gated self-attention, employing a different encoder-decoder architecture, etc. Different types of metadata can help in different ways to improve the quality of the output generated by the transformer and reduce hallucinations. The techniques described in this disclosure can also support multimodal data, such as images, audio, video, or text, etc., with the metadata used representing the specific mode

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

This paper presents the MAXQ approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. The paper defines the MAXQ hierarchy, proves formal results on its representational power, and establishes five conditions for the safe use of state abstractions. The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction. The paper evaluates the MAXQ representation and MAXQ-Q through a series of experiments in three domains and shows experimentally that MAXQ-Q (with state abstractions) converges to a recursively optimal policy much faster than flat Q learning. The fact that MAXQ learns a representation of the value function has an important benefit: it makes it possible to compute and execute an improved, non-hierarchical policy via a procedure similar to the policy improvement step of policy iteration. The paper demonstrates the effectiveness of this non-hierarchical execution experimentally. Finally, the paper concludes with a comparison to related work and a discussion of the design tradeoffs in hierarchical reinforcement learning.Comment: 63 pages, 15 figure