319 research outputs found
Hypergraph Learning with Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level
or hyperedge level, thereby missing the symmetric nature of data co-occurrence,
and resulting in information loss. To address the problem, this paper treats
vertices and hyperedges equally and proposes a new hypergraph formulation named
the \emph{line expansion (LE)} for hypergraphs learning. The new expansion
bijectively induces a homogeneous structure from the hypergraph by treating
vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple
graph, the proposed \emph{line expansion} makes existing graph learning
algorithms compatible with the higher-order structure and has been proven as a
unifying framework for various hypergraph expansions. We evaluate the proposed
line expansion on five hypergraph datasets, the results show that our method
beats SOTA baselines by a significant margin
Aligning Robot and Human Representations
To act in the world, robots rely on a representation of salient task aspects:
for example, to carry a coffee mug, a robot may consider movement efficiency or
mug orientation in its behavior. However, if we want robots to act for and with
people, their representations must not be just functional but also reflective
of what humans care about, i.e. they must be aligned. We observe that current
learning approaches suffer from representation misalignment, where the robot's
learned representation does not capture the human's representation. We suggest
that because humans are the ultimate evaluator of robot performance, we must
explicitly focus our efforts on aligning learned representations with humans,
in addition to learning the downstream task. We advocate that current
representation learning approaches in robotics should be studied from the
perspective of how well they accomplish the objective of representation
alignment. We mathematically define the problem, identify its key desiderata,
and situate current methods within this formalism. We conclude by suggesting
future directions for exploring open challenges.Comment: 14 pages, 3 figures, 1 tabl
- …